# https://s3-us-west-2.amazonaws.com/syr-mac/prod/IST+565+Data+Mining/PDFs/Assignments/Project-instructions-updated-11-27-2017.pdf
# https://archive.ics.uci.edu/ml/datasets/Forest+Fires
# https://towardsdatascience.com/beginners-guide-to-k-nearest-neighbors-in-r-from-zero-to-hero-d92cd4074bdb
# install.packages("ggvis")
# install.packages("plotrix")
# install.packages("ISLR")
# install.packages(“ggplot2”) # install.packages(“plyr”)
# install.packages(“dplyr”) # install.packages(“class”)# Load libraries
# install.packages("tidyverse")
# install.packages("cluster")
# install.packages("factoextra")
# install.packages("randomForest")
# install.packages("pROC")
# install.packages("FSelector")
# install.packages("GGally")
# install.packages("taRifx")
# install.packages("klar")
# install.packages("purrr")
library(purrr)
library(GGally)
## Loading required package: ggplot2
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
library(taRifx)
##
## Attaching package: 'taRifx'
## The following object is masked from 'package:purrr':
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## rep_along
library(pROC)
## Type 'citation("pROC")' for a citation.
##
## Attaching package: 'pROC'
## The following objects are masked from 'package:stats':
##
## cov, smooth, var
library(FSelector)
library(randomForest)
## randomForest 4.6-14
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
## The following object is masked from 'package:ggplot2':
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## margin
library(readr)
library(ggplot2)
library(ISLR)
library(reshape2)
library(plyr)
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## Attaching package: 'plyr'
## The following object is masked from 'package:purrr':
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## compact
library(dplyr)
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## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
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## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following object is masked from 'package:randomForest':
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## combine
## The following objects are masked from 'package:taRifx':
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## between, distinct, first, last
## The following object is masked from 'package:GGally':
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## nasa
## The following objects are masked from 'package:stats':
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## filter, lag
## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union
library(class)
library(ggvis)
##
## Attaching package: 'ggvis'
## The following object is masked from 'package:ggplot2':
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## resolution
library(readxl)
library(plotrix)
library(cluster)
library(factoextra)
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
# library(tidyverse)
library(gridExtra)
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
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## combine
## The following object is masked from 'package:randomForest':
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## combine
library(cluster)
library(reshape2)
library(tidyr)
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## Attaching package: 'tidyr'
## The following object is masked from 'package:reshape2':
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## smiths
library(rpart)
library(rpart.plot)
library(MASS)
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## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
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## select
library(caTools)
library(sqldf)
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
library(corrplot)
## corrplot 0.84 loaded
library(corrgram)
## Registered S3 method overwritten by 'seriation':
## method from
## reorder.hclust gclus
##
## Attaching package: 'corrgram'
## The following object is masked from 'package:plyr':
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## baseball
library(e1071)
library(caret)
## Loading required package: lattice
##
## Attaching package: 'lattice'
## The following object is masked from 'package:corrgram':
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## panel.fill
##
## Attaching package: 'caret'
## The following object is masked from 'package:purrr':
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## lift
library(pROC)
library(CORElearn)
library(RWeka)
##
## Attaching package: 'RWeka'
## The following object is masked from 'package:caTools':
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## LogitBoost
library(FSelector)
# Load files
forestfires <- read_csv("forestfires.csv")
## Parsed with column specification:
## cols(
## month = col_character(),
## day = col_character(),
## X = col_double(),
## Y = col_double(),
## FFMC = col_double(),
## DMC = col_double(),
## DC = col_double(),
## ISI = col_double(),
## temp = col_double(),
## RH = col_double(),
## wind = col_double(),
## rain = col_double(),
## area = col_double()
## )
forestfiresEX <-read_excel("ForestFiresWith.xlsx")
forestfires_na_factor <- read_csv("forestfires.csv")
## Parsed with column specification:
## cols(
## month = col_character(),
## day = col_character(),
## X = col_double(),
## Y = col_double(),
## FFMC = col_double(),
## DMC = col_double(),
## DC = col_double(),
## ISI = col_double(),
## temp = col_double(),
## RH = col_double(),
## wind = col_double(),
## rain = col_double(),
## area = col_double()
## )
# find mean for foest fires
mean(forestfires$area)
## [1] 12.84729
# Feature generation
## IF the area burned is greater than .1 , equals a significant fire
forestfires$fire_yes_no <- ifelse(forestfires$area>0.1,1,0)
# Create a new data frame for newly made significant fire data
#forestfiresmm <- forestfires %>% select(X,Y,month,day,FFMC,DMC,DC,ISI,temp,RH,wind,rain,area,fire_yes_no) %>% filter(forestfires$fire_yes_no == "1")
forestfiresmm <- forestfires %>% filter(forestfires$fire_yes_no == "1")
forestfiresmm
## # A tibble: 269 x 14
## month day X Y FFMC DMC DC ISI temp RH wind rain
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 jul tue 9 9 85.8 48.3 313. 3.9 18 42 2.7 0
## 2 sep tue 1 4 91 130. 693. 7 21.7 38 2.2 0
## 3 sep mon 2 5 90.9 126. 686. 7 21.9 39 1.8 0
## 4 aug wed 1 2 95.5 99.9 513. 13.2 23.3 31 4.5 0
## 5 aug fri 8 6 90.1 108 530. 12.5 21.2 51 8.9 0
## 6 jul sat 1 2 90 51.3 296. 8.7 16.6 53 5.4 0
## 7 aug wed 2 5 95.5 99.9 513. 13.2 23.8 32 5.4 0
## 8 aug thu 6 5 95.2 132. 579. 10.4 27.4 22 4 0
## 9 mar mon 5 4 90.1 39.7 86.6 6.2 13.2 40 5.4 0
## 10 sep tue 8 3 84.4 73.4 672. 3.2 24.2 28 3.6 0
## # … with 259 more rows, and 2 more variables: area <dbl>,
## # fire_yes_no <dbl>
# Scale OG data frame
forestfires.scaled <- forestfires
forestfires.scaled$FFMC <- scale(forestfires$FFMC)
forestfires.scaled$DMC <- scale(forestfires$DMC)
forestfires.scaled$DC <- scale(forestfires$DC)
forestfires.scaled$ISI <- scale(forestfires$ISI)
forestfires.scaled$temp <- scale(forestfires$temp)
forestfires.scaled$RH <- scale(forestfires$RH)
forestfires.scaled$wind <- scale(forestfires$wind)
forestfires.scaled$rain <- scale(forestfires$rain)
forestfires.scaled$area <- scale(forestfires$area)
# Scale significant fire data frame
forestfiresmm.scaled <- forestfiresmm
forestfiresmm.scaled$FFMC <- scale(forestfiresmm.scaled$FFMC)
forestfiresmm.scaled$DMC <- scale(forestfiresmm.scaled$DMC)
forestfiresmm.scaled$DC <- scale(forestfiresmm.scaled$DC)
forestfiresmm.scaled$ISI <- scale(forestfiresmm.scaled$ISI)
forestfiresmm.scaled$temp <- scale(forestfiresmm.scaled$temp)
forestfiresmm.scaled$RH <- scale(forestfiresmm.scaled$RH)
forestfiresmm.scaled$wind <- scale(forestfiresmm.scaled$wind)
forestfiresmm.scaled$rain <- scale(forestfiresmm.scaled$rain)
forestfiresmm.scaled$area <- scale(forestfiresmm.scaled$area)
# View it
View(forestfires)
# Str
str(forestfires)
## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 517 obs. of 14 variables:
## $ month : chr "mar" "oct" "oct" "mar" ...
## $ day : chr "fri" "tue" "sat" "fri" ...
## $ X : num 7 7 7 8 8 8 8 8 8 7 ...
## $ Y : num 5 4 4 6 6 6 6 6 6 5 ...
## $ FFMC : num 86.2 90.6 90.6 91.7 89.3 92.3 92.3 91.5 91 92.5 ...
## $ DMC : num 26.2 35.4 43.7 33.3 51.3 ...
## $ DC : num 94.3 669.1 686.9 77.5 102.2 ...
## $ ISI : num 5.1 6.7 6.7 9 9.6 14.7 8.5 10.7 7 7.1 ...
## $ temp : num 8.2 18 14.6 8.3 11.4 22.2 24.1 8 13.1 22.8 ...
## $ RH : num 51 33 33 97 99 29 27 86 63 40 ...
## $ wind : num 6.7 0.9 1.3 4 1.8 5.4 3.1 2.2 5.4 4 ...
## $ rain : num 0 0 0 0.2 0 0 0 0 0 0 ...
## $ area : num 0 0 0 0 0 0 0 0 0 0 ...
## $ fire_yes_no: num 0 0 0 0 0 0 0 0 0 0 ...
## - attr(*, "spec")=
## .. cols(
## .. month = col_character(),
## .. day = col_character(),
## .. X = col_double(),
## .. Y = col_double(),
## .. FFMC = col_double(),
## .. DMC = col_double(),
## .. DC = col_double(),
## .. ISI = col_double(),
## .. temp = col_double(),
## .. RH = col_double(),
## .. wind = col_double(),
## .. rain = col_double(),
## .. area = col_double()
## .. )
# Descripitive Summary
summary(forestfires)
## month day X Y
## Length:517 Length:517 Min. :1.000 Min. :2.0
## Class :character Class :character 1st Qu.:3.000 1st Qu.:4.0
## Mode :character Mode :character Median :4.000 Median :4.0
## Mean :4.669 Mean :4.3
## 3rd Qu.:7.000 3rd Qu.:5.0
## Max. :9.000 Max. :9.0
## FFMC DMC DC ISI
## Min. :18.70 Min. : 1.1 Min. : 7.9 Min. : 0.000
## 1st Qu.:90.20 1st Qu.: 68.6 1st Qu.:437.7 1st Qu.: 6.500
## Median :91.60 Median :108.3 Median :664.2 Median : 8.400
## Mean :90.64 Mean :110.9 Mean :547.9 Mean : 9.022
## 3rd Qu.:92.90 3rd Qu.:142.4 3rd Qu.:713.9 3rd Qu.:10.800
## Max. :96.20 Max. :291.3 Max. :860.6 Max. :56.100
## temp RH wind rain
## Min. : 2.20 Min. : 15.00 Min. :0.400 Min. :0.00000
## 1st Qu.:15.50 1st Qu.: 33.00 1st Qu.:2.700 1st Qu.:0.00000
## Median :19.30 Median : 42.00 Median :4.000 Median :0.00000
## Mean :18.89 Mean : 44.29 Mean :4.018 Mean :0.02166
## 3rd Qu.:22.80 3rd Qu.: 53.00 3rd Qu.:4.900 3rd Qu.:0.00000
## Max. :33.30 Max. :100.00 Max. :9.400 Max. :6.40000
## area fire_yes_no
## Min. : 0.00 Min. :0.0000
## 1st Qu.: 0.00 1st Qu.:0.0000
## Median : 0.52 Median :1.0000
## Mean : 12.85 Mean :0.5203
## 3rd Qu.: 6.57 3rd Qu.:1.0000
## Max. :1090.84 Max. :1.0000
(head(forestfires,n=5))
## # A tibble: 5 x 14
## month day X Y FFMC DMC DC ISI temp RH wind rain
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 mar fri 7 5 86.2 26.2 94.3 5.1 8.2 51 6.7 0
## 2 oct tue 7 4 90.6 35.4 669. 6.7 18 33 0.9 0
## 3 oct sat 7 4 90.6 43.7 687. 6.7 14.6 33 1.3 0
## 4 mar fri 8 6 91.7 33.3 77.5 9 8.3 97 4 0.2
## 5 mar sun 8 6 89.3 51.3 102. 9.6 11.4 99 1.8 0
## # … with 2 more variables: area <dbl>, fire_yes_no <dbl>
# Save col names in a variable
colnamesff <- colnames(forestfires)
## EDA ##
# Plot unique variables 3d
slices <- c(1:14)
lbls <- colnamesff
#pie3D(slices,labels=lbls,explode=0.2,theta=1,radius = 1, main="Distribution of unique variables")
pie3D(slices,labels=lbls,explode=0.2,theta=1,radius = 1, main="Distribution of unique variables")
# Plot unique variables 2d
colors = c('#4286f4','#bb3af2','#ed2f52','#efc023','#ea7441')
pie(slices, lbls, main='Distribution of unique variables',density=30 ,col=colors, angle=45)
## Check for missing data and make sure no missing data
forestfires[!complete.cases(forestfires),]
## # A tibble: 0 x 14
## # … with 14 variables: month <chr>, day <chr>, X <dbl>, Y <dbl>,
## # FFMC <dbl>, DMC <dbl>, DC <dbl>, ISI <dbl>, temp <dbl>, RH <dbl>,
## # wind <dbl>, rain <dbl>, area <dbl>, fire_yes_no <dbl>
sum(is.na(forestfires))
## [1] 0
# Create a scatter plot with variables FFMC and DC filled by month
## View difference between scaled and not scaled
forestfires_na_factor %>% ggvis(~FFMC, ~DC, fill = ~month) %>% layer_points() # possible 2 or 3 key clusters
forestfires.scaled %>% ggvis(~FFMC, ~DC,fill=~month)%>% layer_points()
# Any cluster between Temp and DC?
forestfires.scaled %>% ggvis(~temp, ~DMC, fill = ~area) %>% layer_points()
## Visual Clusters found in DC and FFMC !!
## 1 months aug, sep, nov
## 2 july, june, DEC
### 3 feb,march, april
# if chr, change to factor using dplyr
#cluster <- select_(forestfires.scaled,-c(X,Y,month,day))
# create a dataframe for clusting
## Only keep of interest variables and drop the rest of them !!
# cluster_scaled <- select(forestfiresmm.scaled,-c(X,Y,month,day,DMC,ISI,temp,RH,wind,rain,area,fire_yes_no))
cluster_scaled <- dplyr::select(forestfiresmm.scaled,c(5,7))
# use scaled data since k means is a distance measure
k1 = kmeans(cluster_scaled,centers = 2, nstart = 25)
k2 = kmeans(cluster_scaled,centers = 3, nstart = 25)
k3 = kmeans(cluster_scaled,centers = 4, nstart = 25)
k4 = kmeans(cluster_scaled,centers = 5, nstart = 25)
k5 = kmeans(cluster_scaled,centers = 6, nstart = 25)
k6 = kmeans(cluster_scaled,centers = 7, nstart = 25)
k7 = kmeans(cluster_scaled,centers = 8, nstart = 25)
# plot to compare
p1 <- fviz_cluster(k1,geom = "point", cluster_scaled)+ggtitle("k=2")
p2 <- fviz_cluster(k2,geom = "point", cluster_scaled)+ggtitle("k=3")
p3 <- fviz_cluster(k3,geom = "point", cluster_scaled)+ggtitle("k=4")
p4 <- fviz_cluster(k4,geom = "point", cluster_scaled)+ggtitle("k=5")
p5 <- fviz_cluster(k5,geom = "point", cluster_scaled)+ggtitle("k=6")
p6 <- fviz_cluster(k6,geom = "point", cluster_scaled)+ggtitle("k=7")
p7 <- fviz_cluster(k7,geom = "point", cluster_scaled)+ggtitle("k=8")
# for a grid layout
grid.arrange(p1,p2,p3,p4,p5,p6,p7,nrow=2)
grid.arrange(p1,p2,p3,nrow=1)
### Analyze the cluster results
# Function to compute total within cluster sum of square
wss = function(k){kmeans(cluster_scaled,k,nstart = 10)$tot.withinss}
#Compute and plot wss for k =1 to k =15
k.values = 1:15
# Extract wsss for 2-15 clusters
wss_values = map_dbl(k.values,wss)
plot(k.values, wss_values,
type = "b", pch = 19, frame = FALSE,
main="Elbow Plot of K-Means Clustering",
xlab="Number of Clusters K",
ylab="Total within-clusters sum of squares")
# Silhoette scores
silhouette_score = function(k){
km = kmeans(cluster_scaled, centers = k, nstart = 25)
ss = silhouette(km$cluster,dist(cluster_scaled))
mean(ss[,3])
}
k=2:10
avg_sil = sapply(k,silhouette_score)
plot(k,type = 'b',avg_sil,xlab = 'number of clusters',ylab = 'average silhouette scores', main="Silhouette Plot of K-Means Clustering",frame ='False')
# Gap statistic
fviz_nbclust(cluster_scaled,kmeans,method = "gap_stat")
# --> shows 2 optimal clusters
## View stats within a cluster
cluster_2 <- kmeans(cluster_scaled,centers = 2,nstart = 10)
cluster_2$cluster <- as.factor(cluster_2$cluster)
cluster_2
## K-means clustering with 2 clusters of sizes 60, 209
##
## Cluster means:
## FFMC DC
## 1 -1.2621146 -1.5100174
## 2 0.3623295 0.4334978
##
## Clustering vector:
## [1] 1 2 2 2 2 1 2 2 1 1 2 2 2 1 2 2 2 2 2 2 2 2 1 2 1 2 2 1 2 2 1 2 2 1 2
## [36] 2 2 2 1 2 2 2 2 1 1 2 2 2 1 2 1 1 1 2 2 2 2 2 1 2 2 1 2 1 1 2 1 2 2 2
## [71] 2 2 2 2 2 1 1 1 2 2 2 1 2 2 1 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2
## [106] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2
## [141] 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [176] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 1 2 2 2
## [211] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 2 2 2
## [246] 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1
## Levels: 1 2
##
## Within cluster sum of squares by cluster:
## [1] 151.7523 85.1491
## (between_SS / total_SS = 55.8 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
cluster_3 <- kmeans(cluster_scaled,centers = 3,nstart = 10)
cluster_3$cluster <- as.factor(cluster_3$cluster)
cluster_3
## K-means clustering with 3 clusters of sizes 207, 23, 39
##
## Cluster means:
## FFMC DC
## 1 0.3603208 0.4487290
## 2 -2.3744327 -0.7942261
## 3 -0.5121656 -1.9133255
##
## Clustering vector:
## [1] 2 1 1 1 1 3 1 1 3 2 1 1 1 3 1 1 1 1 1 1 1 1 3 1 3 1 1 3 1 1 3 1 1 2 1
## [36] 1 1 1 2 1 1 1 1 2 3 1 1 1 3 1 3 3 3 1 1 1 1 1 2 1 1 2 1 3 3 1 3 1 1 1
## [71] 1 1 1 1 1 3 3 3 1 1 1 3 1 1 3 3 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1
## [106] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 1 1 1 1
## [141] 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [176] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 3 1 1 3 1 1 1 1 3 1 1 1
## [211] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 2 2 2 3 3 3 3 3 3 3 3 1 1
## [246] 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
## Levels: 1 2 3
##
## Within cluster sum of squares by cluster:
## [1] 80.03692 65.03808 25.18623
## (between_SS / total_SS = 68.2 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
cluster_4 <- kmeans(cluster_scaled,centers = 4,nstart = 10)
cluster_4$cluster <- as.factor(cluster_4$cluster)
cluster_4
## K-means clustering with 4 clusters of sizes 23, 72, 137, 37
##
## Cluster means:
## FFMC DC
## 1 -2.3744327 -0.79422614
## 2 0.8120772 0.05189641
## 3 0.1259658 0.63404748
## 4 -0.5706735 -1.95496878
##
## Clustering vector:
## [1] 1 3 3 2 3 4 2 2 4 1 2 2 2 4 2 2 3 2 3 3 3 3 4 2 4 3 3 4 3 2 4 2 3 1 3
## [36] 3 3 3 1 3 3 3 3 1 4 3 2 3 4 3 4 4 4 2 2 3 2 2 1 3 3 1 3 4 4 3 4 3 2 3
## [71] 3 2 3 2 2 4 4 4 3 3 3 4 3 3 4 4 3 3 3 2 3 2 3 3 3 1 3 3 3 3 3 2 3 3 2
## [106] 2 2 2 2 2 3 2 2 3 2 3 3 3 2 2 2 3 3 3 3 1 1 1 1 1 1 1 1 1 4 4 2 2 2 2
## [141] 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [176] 3 3 3 3 3 3 3 3 3 3 3 2 2 3 2 3 3 3 3 2 4 3 3 4 3 3 2 3 3 3 3 4 2 2 2
## [211] 3 3 3 3 3 2 3 3 3 3 3 2 3 3 4 3 2 3 3 2 3 3 1 1 1 4 4 4 4 4 4 4 2 2 2
## [246] 2 2 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 1 1 1
## Levels: 1 2 3 4
##
## Within cluster sum of squares by cluster:
## [1] 65.03808 22.60481 24.33197 21.45854
## (between_SS / total_SS = 75.1 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
cluster_5 <- kmeans(cluster_scaled,centers = 5,nstart = 10)
cluster_5$cluster <- as.factor(cluster_5$cluster)
cluster_5
## K-means clustering with 5 clusters of sizes 4, 141, 64, 49, 11
##
## Cluster means:
## FFMC DC
## 1 -4.7720552 -1.702911773
## 2 0.1830428 0.622243620
## 3 0.8338975 -0.007036539
## 4 -0.8641367 -1.765087342
## 5 -1.6134145 0.546810450
##
## Clustering vector:
## [1] 4 2 2 3 2 4 3 3 4 5 3 3 3 4 3 3 2 3 2 2 2 2 4 3 4 2 2 4 2 3 4 3 2 5 2
## [36] 2 2 2 4 2 2 2 2 5 4 2 3 2 4 2 4 4 4 3 3 2 3 3 4 2 2 1 2 4 4 2 4 2 3 2
## [71] 2 3 2 3 3 4 4 4 2 2 2 4 2 2 4 4 2 2 2 3 2 3 2 2 2 5 2 2 2 2 2 3 2 2 3
## [106] 2 2 2 2 3 2 3 3 2 3 2 2 2 3 3 3 2 2 2 2 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3
## [141] 4 4 2 2 2 2 2 2 2 2 2 2 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [176] 2 2 5 2 2 2 2 2 2 2 2 2 3 2 3 2 2 2 2 3 4 2 2 4 2 2 3 2 5 5 2 4 3 3 3
## [211] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 2 3 2 2 2 2 2 1 1 1 4 4 4 4 4 4 4 3 3 3
## [246] 3 3 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 5 5 5
## Levels: 1 2 3 4 5
##
## Within cluster sum of squares by cluster:
## [1] 16.174952 21.818887 20.189557 44.367355 5.760022
## (between_SS / total_SS = 79.8 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
cluster_6 <- kmeans(cluster_scaled,centers = 6,nstart = 10)
cluster_6$cluster <- as.factor(cluster_6$cluster)
cluster_6
## K-means clustering with 6 clusters of sizes 36, 6, 17, 49, 58, 103
##
## Cluster means:
## FFMC DC
## 1 -0.5728355 -1.99253193
## 2 -4.0364254 -1.88103378
## 3 -1.7878471 -0.41064697
## 4 -0.1374781 0.32913614
## 5 0.8904067 0.00130322
## 6 0.2944356 0.71645722
##
## Clustering vector:
## [1] 3 4 4 5 4 1 5 5 1 3 5 6 5 1 4 5 6 5 6 6 6 6 1 5 1 6 4 1 6 5 1 5 6 3 4
## [36] 4 4 4 2 4 4 4 6 3 1 6 5 4 1 4 1 1 1 5 5 6 5 5 2 6 4 2 4 1 1 4 1 6 5 4
## [71] 6 6 4 5 5 1 1 1 6 4 6 1 4 6 1 1 4 6 6 5 6 5 6 6 4 3 6 4 6 4 6 5 6 6 5
## [106] 6 6 6 6 4 4 5 5 6 5 4 4 4 5 5 5 6 6 6 6 3 3 3 3 3 3 3 3 3 1 1 4 4 5 5
## [141] 1 1 4 4 6 6 6 6 6 6 6 6 4 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [176] 6 6 4 6 6 6 6 6 6 6 6 6 5 4 5 6 6 4 6 5 1 6 6 1 6 4 5 6 4 4 6 1 5 5 5
## [211] 6 4 4 4 6 6 6 4 4 6 6 6 6 6 1 6 5 4 4 6 6 6 2 2 2 1 1 1 1 1 1 1 5 5 5
## [246] 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 3 3 3
## Levels: 1 2 3 4 5 6
##
## Within cluster sum of squares by cluster:
## [1] 19.572879 23.049495 9.977696 11.341690 16.239870 10.868578
## (between_SS / total_SS = 83.0 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
cluster_7 <- kmeans(cluster_scaled,centers = 7,nstart = 10)
cluster_7$cluster <- as.factor(cluster_7$cluster)
cluster_7
## K-means clustering with 7 clusters of sizes 129, 24, 26, 1, 11, 61, 17
##
## Cluster means:
## FFMC DC
## 1 0.1002921 0.6386159
## 2 0.2472720 -0.6657184
## 3 -0.3675930 -2.0570902
## 4 -7.4095568 0.4121876
## 5 -2.4550662 -2.3112612
## 6 0.9097359 0.3126691
## 7 -1.7878471 -0.4106470
##
## Clustering vector:
## [1] 7 1 1 6 2 2 6 6 3 7 6 6 2 2 2 6 6 6 6 1 1 1 3 6 3 1 1 5 1 6 3 6 1 7 1
## [36] 1 1 1 5 1 1 1 1 7 3 1 6 1 3 1 3 3 3 6 6 1 6 6 5 1 1 4 1 3 5 1 3 1 6 1
## [71] 1 6 1 6 6 3 3 3 1 1 1 3 1 6 3 3 1 6 1 2 6 2 1 6 1 7 1 1 1 1 1 6 1 1 2
## [106] 6 6 6 6 2 1 6 6 1 6 1 1 1 6 6 6 1 1 1 1 7 7 7 7 7 7 7 7 7 3 5 2 2 2 2
## [141] 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [176] 1 1 1 1 1 1 1 1 1 1 1 6 2 1 6 1 1 1 1 6 5 1 1 5 1 1 2 1 1 1 1 5 2 6 6
## [211] 1 1 1 1 1 6 1 1 1 6 1 6 1 1 3 1 6 1 1 6 1 1 5 5 5 3 3 3 3 3 3 3 2 2 2
## [246] 2 2 2 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 7 7 7
## Levels: 1 2 3 4 5 6 7
##
## Within cluster sum of squares by cluster:
## [1] 21.487796 5.950791 6.588888 0.000000 10.730567 7.930224 9.977696
## (between_SS / total_SS = 88.3 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
# cluster_2df <- as.data.frame.complex(cluster_2)
# ggplot(cluster_2, aes(color=cluster_2$cluster))+geom_point()
# ggplot(cluster_3, aes(W1,W44,color =cluster_3$cluster)) +geom_point()
# View counts within cluster
group1 = data.frame(t(cluster_scaled[cluster_3$cluster == 3,]))
summary(sapply(group1, mean))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.1284 -1.5222 -1.1758 -1.2127 -0.9731 -0.2859
hist(sapply(group1, mean), main = "Histogram of Group 3", xlab = "Number of observations")
## Create a training a test set with scaled data
ind <- sample(2, nrow(forestfires.scaled), replace=TRUE, prob=c(0.67, 0.33)) # Randomize (SHUFFLE) data
forestfires.scaled.training <- forestfires.scaled[ind==1, 4:11]
forestfires.scaled.test <- forestfires.scaled[ind==2, 4:11]
forestfires.scaled.trainLabels <- forestfires.scaled[ind==1, 3]
forestfires.scaled.testLabels <- forestfires.scaled[ind==2, 3]
## MORE TRANSFORMATION of the data using DPLYR
# Change OG data into factor
# if numeric, change to factor using dplyr
forestfires <- forestfires %>%
mutate_if(is.numeric,funs(as.factor))
## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.
str(forestfires)
## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 517 obs. of 14 variables:
## $ month : chr "mar" "oct" "oct" "mar" ...
## $ day : chr "fri" "tue" "sat" "fri" ...
## $ X : Factor w/ 9 levels "1","2","3","4",..: 7 7 7 8 8 8 8 8 8 7 ...
## $ Y : Factor w/ 7 levels "2","3","4","5",..: 4 3 3 5 5 5 5 5 5 4 ...
## $ FFMC : Factor w/ 106 levels "18.7","50.4",..: 29 57 57 68 47 74 74 66 61 76 ...
## $ DMC : Factor w/ 215 levels "1.1","2.4","3",..: 38 50 57 49 67 94 99 168 150 96 ...
## $ DC : Factor w/ 219 levels "7.9","9.3","15.3",..: 42 145 157 34 47 92 93 119 162 165 ...
## $ ISI : Factor w/ 119 levels "0","0.4","0.7",..: 30 43 43 65 69 103 60 77 45 46 ...
## $ temp : Factor w/ 192 levels "2.2","4.2","4.6",..: 13 86 56 14 31 126 145 12 43 132 ...
## $ RH : Factor w/ 75 levels "15","17","18",..: 35 17 17 73 74 13 11 67 47 24 ...
## $ wind : Factor w/ 21 levels "0.4","0.9","1.3",..: 15 2 3 9 4 12 7 5 12 9 ...
## $ rain : Factor w/ 7 levels "0","0.2","0.4",..: 1 1 1 2 1 1 1 1 1 1 ...
## $ area : Factor w/ 251 levels "0","0.09","0.17",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ fire_yes_no: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
# if chr, change to factor using dplyr
forestfires <- forestfires %>%
mutate_if(is.character,funs(as.factor))
str(forestfires)
## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 517 obs. of 14 variables:
## $ month : Factor w/ 12 levels "apr","aug","dec",..: 8 11 11 8 8 2 2 2 12 12 ...
## $ day : Factor w/ 7 levels "fri","mon","sat",..: 1 6 3 1 4 4 2 2 6 3 ...
## $ X : Factor w/ 9 levels "1","2","3","4",..: 7 7 7 8 8 8 8 8 8 7 ...
## $ Y : Factor w/ 7 levels "2","3","4","5",..: 4 3 3 5 5 5 5 5 5 4 ...
## $ FFMC : Factor w/ 106 levels "18.7","50.4",..: 29 57 57 68 47 74 74 66 61 76 ...
## $ DMC : Factor w/ 215 levels "1.1","2.4","3",..: 38 50 57 49 67 94 99 168 150 96 ...
## $ DC : Factor w/ 219 levels "7.9","9.3","15.3",..: 42 145 157 34 47 92 93 119 162 165 ...
## $ ISI : Factor w/ 119 levels "0","0.4","0.7",..: 30 43 43 65 69 103 60 77 45 46 ...
## $ temp : Factor w/ 192 levels "2.2","4.2","4.6",..: 13 86 56 14 31 126 145 12 43 132 ...
## $ RH : Factor w/ 75 levels "15","17","18",..: 35 17 17 73 74 13 11 67 47 24 ...
## $ wind : Factor w/ 21 levels "0.4","0.9","1.3",..: 15 2 3 9 4 12 7 5 12 9 ...
## $ rain : Factor w/ 7 levels "0","0.2","0.4",..: 1 1 1 2 1 1 1 1 1 1 ...
## $ area : Factor w/ 251 levels "0","0.09","0.17",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ fire_yes_no: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
# Nice viz to view any null values
# R for Loop , for value in sequence :: names gives name of a set of object
#:: CAT concatenate and print :: sum,values that are na, in subset of data
colnames(forestfires)
## [1] "month" "day" "X" "Y" "FFMC"
## [6] "DMC" "DC" "ISI" "temp" "RH"
## [11] "wind" "rain" "area" "fire_yes_no"
colname <- colnames(forestfires)
for(colname in names(forestfires)){
cat("\n","\n Looking at column...", colname)
NAcount <- sum(is.na(forestfires[colname]))
cat("\nThe num of missing values in column ", colname, "is ", NAcount)
}
##
##
## Looking at column... month
## The num of missing values in column month is 0
##
## Looking at column... day
## The num of missing values in column day is 0
##
## Looking at column... X
## The num of missing values in column X is 0
##
## Looking at column... Y
## The num of missing values in column Y is 0
##
## Looking at column... FFMC
## The num of missing values in column FFMC is 0
##
## Looking at column... DMC
## The num of missing values in column DMC is 0
##
## Looking at column... DC
## The num of missing values in column DC is 0
##
## Looking at column... ISI
## The num of missing values in column ISI is 0
##
## Looking at column... temp
## The num of missing values in column temp is 0
##
## Looking at column... RH
## The num of missing values in column RH is 0
##
## Looking at column... wind
## The num of missing values in column wind is 0
##
## Looking at column... rain
## The num of missing values in column rain is 0
##
## Looking at column... area
## The num of missing values in column area is 0
##
## Looking at column... fire_yes_no
## The num of missing values in column fire_yes_no is 0
##### Melt data frame ######
# Drop useless coloumns
forestfires.scaled <- dplyr::select(forestfires.scaled,c(-1,-2))
mdata <- melt(forestfiresmm.scaled,id=c("month","day"))
## Warning: attributes are not identical across measure variables; they will
## be dropped
mdata %>% drop_na()
## month day variable value
## 1 jul tue X 9.0000000000
## 2 sep tue X 1.0000000000
## 3 sep mon X 2.0000000000
## 4 aug wed X 1.0000000000
## 5 aug fri X 8.0000000000
## 6 jul sat X 1.0000000000
## 7 aug wed X 2.0000000000
## 8 aug thu X 6.0000000000
## 9 mar mon X 5.0000000000
## 10 sep tue X 8.0000000000
## 11 aug tue X 2.0000000000
## 12 sep thu X 8.0000000000
## 13 jun fri X 6.0000000000
## 14 jul sun X 9.0000000000
## 15 jul sat X 3.0000000000
## 16 sep fri X 5.0000000000
## 17 sep sat X 1.0000000000
## 18 aug sun X 7.0000000000
## 19 sep sat X 2.0000000000
## 20 aug wed X 2.0000000000
## 21 aug wed X 2.0000000000
## 22 sep fri X 7.0000000000
## 23 mar mon X 7.0000000000
## 24 aug thu X 6.0000000000
## 25 mar sat X 6.0000000000
## 26 sep sat X 8.0000000000
## 27 sep sun X 8.0000000000
## 28 mar thu X 6.0000000000
## 29 aug wed X 6.0000000000
## 30 aug wed X 6.0000000000
## 31 mar fri X 6.0000000000
## 32 aug thu X 8.0000000000
## 33 sep wed X 5.0000000000
## 34 aug wed X 8.0000000000
## 35 aug sun X 7.0000000000
## 36 sep mon X 4.0000000000
## 37 aug sat X 1.0000000000
## 38 aug sat X 1.0000000000
## 39 apr thu X 6.0000000000
## 40 aug sun X 2.0000000000
## 41 sep wed X 2.0000000000
## 42 aug tue X 8.0000000000
## 43 sep sun X 1.0000000000
## 44 oct mon X 8.0000000000
## 45 feb sun X 5.0000000000
## 46 oct mon X 7.0000000000
## 47 aug fri X 8.0000000000
## 48 sep tue X 2.0000000000
## 49 mar sun X 8.0000000000
## 50 sep mon X 1.0000000000
## 51 mar sat X 6.0000000000
## 52 mar sun X 7.0000000000
## 53 mar fri X 6.0000000000
## 54 aug thu X 2.0000000000
## 55 aug tue X 2.0000000000
## 56 sep wed X 4.0000000000
## 57 aug tue X 2.0000000000
## 58 aug fri X 2.0000000000
## 59 apr thu X 6.0000000000
## 60 sep thu X 4.0000000000
## 61 sep tue X 3.0000000000
## 62 sep mon X 2.0000000000
## 63 sep tue X 1.0000000000
## 64 mar sun X 6.0000000000
## 65 feb sun X 7.0000000000
## 66 oct wed X 8.0000000000
## 67 mar sat X 5.0000000000
## 68 sep thu X 4.0000000000
## 69 aug sat X 2.0000000000
## 70 sep tue X 7.0000000000
## 71 sep fri X 6.0000000000
## 72 sep thu X 8.0000000000
## 73 oct sat X 4.0000000000
## 74 aug sat X 7.0000000000
## 75 sep fri X 7.0000000000
## 76 mar mon X 7.0000000000
## 77 mar sat X 4.0000000000
## 78 mar sat X 4.0000000000
## 79 sep sun X 4.0000000000
## 80 sep mon X 1.0000000000
## 81 sep wed X 4.0000000000
## 82 mar mon X 6.0000000000
## 83 aug sun X 8.0000000000
## 84 sep fri X 3.0000000000
## 85 mar mon X 4.0000000000
## 86 jul fri X 2.0000000000
## 87 sep wed X 7.0000000000
## 88 sep sun X 4.0000000000
## 89 oct mon X 7.0000000000
## 90 aug sat X 8.0000000000
## 91 sep sun X 4.0000000000
## 92 aug sat X 8.0000000000
## 93 sep wed X 4.0000000000
## 94 sep sun X 1.0000000000
## 95 sep tue X 6.0000000000
## 96 sep tue X 9.0000000000
## 97 sep sat X 4.0000000000
## 98 aug sun X 8.0000000000
## 99 sep sat X 2.0000000000
## 100 sep tue X 1.0000000000
## 101 sep sat X 6.0000000000
## 102 aug sun X 2.0000000000
## 103 aug sun X 2.0000000000
## 104 aug sun X 3.0000000000
## 105 aug wed X 2.0000000000
## 106 aug wed X 3.0000000000
## 107 aug wed X 8.0000000000
## 108 aug wed X 8.0000000000
## 109 aug wed X 6.0000000000
## 110 aug thu X 7.0000000000
## 111 aug thu X 6.0000000000
## 112 aug sat X 8.0000000000
## 113 aug sat X 4.0000000000
## 114 aug sat X 7.0000000000
## 115 aug mon X 2.0000000000
## 116 aug fri X 3.0000000000
## 117 aug fri X 2.0000000000
## 118 aug fri X 6.0000000000
## 119 aug fri X 4.0000000000
## 120 aug tue X 4.0000000000
## 121 aug tue X 6.0000000000
## 122 aug tue X 4.0000000000
## 123 aug tue X 2.0000000000
## 124 aug tue X 8.0000000000
## 125 aug tue X 2.0000000000
## 126 dec sun X 4.0000000000
## 127 dec wed X 8.0000000000
## 128 dec thu X 4.0000000000
## 129 dec mon X 4.0000000000
## 130 dec mon X 3.0000000000
## 131 dec mon X 4.0000000000
## 132 dec mon X 4.0000000000
## 133 dec fri X 4.0000000000
## 134 dec tue X 6.0000000000
## 135 feb wed X 3.0000000000
## 136 feb fri X 5.0000000000
## 137 jul sat X 9.0000000000
## 138 jul fri X 4.0000000000
## 139 jul tue X 7.0000000000
## 140 jul tue X 8.0000000000
## 141 jun sun X 6.0000000000
## 142 jun mon X 6.0000000000
## 143 sep sun X 7.0000000000
## 144 sep sun X 3.0000000000
## 145 sep sun X 6.0000000000
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## 147 sep thu X 4.0000000000
## 148 sep thu X 5.0000000000
## 149 sep thu X 6.0000000000
## 150 sep thu X 1.0000000000
## 151 sep thu X 6.0000000000
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## 153 sep thu X 6.0000000000
## 154 sep sat X 4.0000000000
## 155 sep sat X 3.0000000000
## 156 sep sat X 7.0000000000
## 157 sep sat X 4.0000000000
## 158 sep mon X 1.0000000000
## 159 sep mon X 6.0000000000
## 160 sep mon X 8.0000000000
## 161 sep mon X 2.0000000000
## 162 sep mon X 2.0000000000
## 163 sep mon X 8.0000000000
## 164 sep mon X 6.0000000000
## 165 sep mon X 2.0000000000
## 166 sep mon X 1.0000000000
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## 170 sep fri X 7.0000000000
## 171 sep fri X 7.0000000000
## 172 sep fri X 7.0000000000
## 173 sep fri X 4.0000000000
## 174 sep fri X 4.0000000000
## 175 sep fri X 1.0000000000
## 176 sep fri X 6.0000000000
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## 178 sep fri X 7.0000000000
## 179 sep tue X 4.0000000000
## 180 sep tue X 6.0000000000
## 181 sep tue X 6.0000000000
## 182 sep tue X 4.0000000000
## 183 sep sat X 6.0000000000
## 184 sep sun X 7.0000000000
## 185 sep fri X 6.0000000000
## 186 sep sat X 6.0000000000
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## 195 aug fri X 6.0000000000
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## 201 aug sun X 5.0000000000
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## 205 sep sat X 6.0000000000
## 206 sep fri X 4.0000000000
## 207 feb fri X 7.0000000000
## 208 jul mon X 9.0000000000
## 209 aug thu X 8.0000000000
## 210 jul tue X 6.0000000000
## 211 aug sun X 2.0000000000
## 212 aug sun X 2.0000000000
## 213 aug wed X 8.0000000000
## 214 jul sun X 8.0000000000
## 215 sep sat X 1.0000000000
## 216 aug sat X 8.0000000000
## 217 aug mon X 2.0000000000
## 218 aug sun X 3.0000000000
## 219 aug sat X 1.0000000000
## 220 aug sun X 2.0000000000
## 221 aug mon X 8.0000000000
## 222 aug sat X 2.0000000000
## 223 sep fri X 1.0000000000
## 224 aug mon X 8.0000000000
## 225 apr mon X 6.0000000000
## 226 sep fri X 2.0000000000
## 227 aug wed X 4.0000000000
## 228 aug fri X 1.0000000000
## 229 aug wed X 1.0000000000
## 230 aug sat X 8.0000000000
## 231 aug sat X 7.0000000000
## 232 sep sun X 1.0000000000
## 233 feb tue X 6.0000000000
## 234 feb tue X 6.0000000000
## 235 feb sat X 2.0000000000
## 236 mar mon X 6.0000000000
## 237 mar wed X 3.0000000000
## 238 mar thu X 6.0000000000
## 239 apr sun X 6.0000000000
## 240 may fri X 4.0000000000
## 241 jun mon X 8.0000000000
## 242 jun sat X 9.0000000000
## 243 jun thu X 4.0000000000
## 244 jun thu X 2.0000000000
## 245 jul thu X 4.0000000000
## 246 jul sun X 4.0000000000
## 247 jul sun X 7.0000000000
## 248 jul mon X 7.0000000000
## 249 jul thu X 9.0000000000
## 250 aug sun X 3.0000000000
## 251 aug sun X 2.0000000000
## 252 aug mon X 2.0000000000
## 253 aug tue X 5.0000000000
## 254 aug tue X 5.0000000000
## 255 aug tue X 4.0000000000
## 256 aug fri X 1.0000000000
## 257 aug sat X 6.0000000000
## 258 aug mon X 4.0000000000
## 259 aug tue X 3.0000000000
## 260 aug tue X 6.0000000000
## 261 aug tue X 7.0000000000
## 262 aug wed X 2.0000000000
## 263 aug wed X 4.0000000000
## 264 aug thu X 1.0000000000
## 265 aug fri X 5.0000000000
## 266 aug fri X 6.0000000000
## 267 aug sun X 4.0000000000
## 268 aug sun X 2.0000000000
## 269 aug sun X 7.0000000000
## 270 jul tue Y 9.0000000000
## 271 sep tue Y 4.0000000000
## 272 sep mon Y 5.0000000000
## 273 aug wed Y 2.0000000000
## 274 aug fri Y 6.0000000000
## 275 jul sat Y 2.0000000000
## 276 aug wed Y 5.0000000000
## 277 aug thu Y 5.0000000000
## 278 mar mon Y 4.0000000000
## 279 sep tue Y 3.0000000000
## 280 aug tue Y 2.0000000000
## 281 sep thu Y 6.0000000000
## 282 jun fri Y 5.0000000000
## 283 jul sun Y 9.0000000000
## 284 jul sat Y 4.0000000000
## 285 sep fri Y 4.0000000000
## 286 sep sat Y 5.0000000000
## 287 aug sun Y 4.0000000000
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## 289 aug wed Y 2.0000000000
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## 291 sep fri Y 4.0000000000
## 292 mar mon Y 4.0000000000
## 293 aug thu Y 4.0000000000
## 294 mar sat Y 3.0000000000
## 295 sep sat Y 6.0000000000
## 296 sep sun Y 5.0000000000
## 297 mar thu Y 5.0000000000
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## 300 mar fri Y 5.0000000000
## 301 aug thu Y 6.0000000000
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## 305 sep mon Y 4.0000000000
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## 315 oct mon Y 4.0000000000
## 316 aug fri Y 6.0000000000
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## 318 mar sun Y 6.0000000000
## 319 sep mon Y 5.0000000000
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## 331 sep mon Y 4.0000000000
## 332 sep tue Y 5.0000000000
## 333 mar sun Y 5.0000000000
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## 337 sep thu Y 5.0000000000
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## 348 sep sun Y 4.0000000000
## 349 sep mon Y 3.0000000000
## 350 sep wed Y 5.0000000000
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## 352 aug sun Y 6.0000000000
## 353 sep fri Y 4.0000000000
## 354 mar mon Y 3.0000000000
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## 357 sep sun Y 4.0000000000
## 358 oct mon Y 5.0000000000
## 359 aug sat Y 6.0000000000
## 360 sep sun Y 6.0000000000
## 361 aug sat Y 6.0000000000
## 362 sep wed Y 4.0000000000
## 363 sep sun Y 5.0000000000
## 364 sep tue Y 4.0000000000
## 365 sep tue Y 4.0000000000
## 366 sep sat Y 5.0000000000
## 367 aug sun Y 6.0000000000
## 368 sep sat Y 2.0000000000
## 369 sep tue Y 2.0000000000
## 370 sep sat Y 5.0000000000
## 371 aug sun Y 4.0000000000
## 372 aug sun Y 4.0000000000
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## 374 aug wed Y 4.0000000000
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## 384 aug mon Y 4.0000000000
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## 394 aug tue Y 5.0000000000
## 395 dec sun Y 6.0000000000
## 396 dec wed Y 6.0000000000
## 397 dec thu Y 6.0000000000
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## 399 dec mon Y 4.0000000000
## 400 dec mon Y 4.0000000000
## 401 dec mon Y 4.0000000000
## 402 dec fri Y 6.0000000000
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## 404 feb wed Y 4.0000000000
## 405 feb fri Y 4.0000000000
## 406 jul sat Y 4.0000000000
## 407 jul fri Y 5.0000000000
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## 411 jun mon Y 5.0000000000
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## 416 sep thu Y 4.0000000000
## 417 sep thu Y 4.0000000000
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## 419 sep thu Y 4.0000000000
## 420 sep thu Y 5.0000000000
## 421 sep thu Y 5.0000000000
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## 1502 sep sat ISI -0.1591753167
## 1503 sep mon ISI 0.0820537548
## 1504 sep mon ISI -0.7140021811
## 1505 sep mon ISI -0.7140021811
## 1506 sep mon ISI -0.7140021811
## 1507 sep mon ISI -0.7140021811
## 1508 sep mon ISI -0.4004043882
## 1509 sep mon ISI -0.4004043882
## 1510 sep mon ISI -0.4004043882
## 1511 sep mon ISI -0.4004043882
## 1512 sep fri ISI 0.1061766620
## 1513 sep fri ISI 0.1061766620
## 1514 sep fri ISI 0.1061766620
## 1515 sep fri ISI 0.1061766620
## 1516 sep fri ISI 0.1061766620
## 1517 sep fri ISI 0.1061766620
## 1518 sep fri ISI 0.1061766620
## 1519 sep fri ISI 0.1061766620
## 1520 sep fri ISI 0.2509141048
## 1521 sep fri ISI 0.2509141048
## 1522 sep fri ISI 0.2509141048
## 1523 sep fri ISI 0.5886348049
## 1524 sep tue ISI -0.6416334597
## 1525 sep tue ISI -0.6416334597
## 1526 sep tue ISI -0.6416334597
## 1527 sep tue ISI 0.8057409693
## 1528 sep sat ISI -0.1832982238
## 1529 sep sun ISI -0.4968960168
## 1530 sep fri ISI -0.4245272953
## 1531 sep sat ISI -0.1832982238
## 1532 aug sat ISI -0.1832982238
## 1533 jul wed ISI 0.6610035264
## 1534 aug thu ISI -0.5692647382
## 1535 aug wed ISI 2.1325008625
## 1536 aug thu ISI -0.6898792740
## 1537 aug sat ISI -0.3521585739
## 1538 aug sun ISI -0.3762814810
## 1539 sep sun ISI 0.5403889906
## 1540 aug fri ISI 0.6851264335
## 1541 feb mon ISI -1.2205832313
## 1542 sep fri ISI -0.4727731096
## 1543 sep sun ISI -0.4968960168
## 1544 feb sun ISI -1.3653206742
## 1545 sep sun ISI 0.5403889906
## 1546 aug sun ISI -0.3762814810
## 1547 jun wed ISI 1.1675845765
## 1548 sep thu ISI -0.2074211310
## 1549 sep wed ISI -1.3170748599
## 1550 sep sat ISI -1.2447061384
## 1551 sep fri ISI -0.4245272953
## 1552 feb fri ISI -1.4135664885
## 1553 jul mon ISI 0.1544224762
## 1554 aug thu ISI 1.1434616694
## 1555 jul tue ISI -0.0626836881
## 1556 aug sun ISI -0.2556669453
## 1557 aug sun ISI -0.3762814810
## 1558 aug wed ISI -0.3280356667
## 1559 jul sun ISI -0.9552312526
## 1560 sep sat ISI -0.1832982238
## 1561 aug sat ISI -0.1832982238
## 1562 aug mon ISI -0.2315440381
## 1563 aug sun ISI -0.3762814810
## 1564 aug sat ISI 0.1061766620
## 1565 aug sun ISI 0.2267911977
## 1566 aug mon ISI -0.2315440381
## 1567 aug sat ISI -0.1832982238
## 1568 sep fri ISI -0.4727731096
## 1569 aug mon ISI -0.2315440381
## 1570 apr mon ISI -1.3170748599
## 1571 sep fri ISI -0.4245272953
## 1572 aug wed ISI 2.1325008625
## 1573 aug fri ISI 1.7224114410
## 1574 aug wed ISI -0.3280356667
## 1575 aug sat ISI -0.1832982238
## 1576 aug sat ISI -0.3521585739
## 1577 sep sun ISI -0.4968960168
## 1578 feb tue ISI -1.7512871885
## 1579 feb tue ISI -1.7512871885
## 1580 feb sat ISI -1.7754100957
## 1581 mar mon ISI -0.4968960168
## 1582 mar wed ISI -0.4486502025
## 1583 mar thu ISI -0.1591753167
## 1584 apr sun ISI 0.7574951550
## 1585 may fri ISI -0.8346167169
## 1586 jun mon ISI -1.0758457884
## 1587 jun sat ISI 0.0579308477
## 1588 jun thu ISI 0.3956515477
## 1589 jun thu ISI 2.1325008625
## 1590 jul thu ISI 0.1785453834
## 1591 jul sun ISI 1.3364449266
## 1592 jul sun ISI 1.3364449266
## 1593 jul mon ISI -0.6657563668
## 1594 jul thu ISI 0.0820537548
## 1595 aug sun ISI 1.1917074837
## 1596 aug sun ISI 1.1917074837
## 1597 aug mon ISI 2.9285567984
## 1598 aug tue ISI 2.0601321410
## 1599 aug tue ISI 2.0601321410
## 1600 aug tue ISI 2.0601321410
## 1601 aug fri ISI 0.5162660835
## 1602 aug sat ISI 1.1675845765
## 1603 aug mon ISI 1.8430259767
## 1604 aug tue ISI 1.2399532980
## 1605 aug tue ISI 1.2399532980
## 1606 aug tue ISI 1.2399532980
## 1607 aug wed ISI 2.6149590055
## 1608 aug wed ISI 2.6149590055
## 1609 aug thu ISI 0.2267911977
## 1610 aug fri ISI -0.4968960168
## 1611 aug fri ISI -0.4968960168
## 1612 aug sun ISI -1.7512871885
## 1613 aug sun ISI -1.7512871885
## 1614 aug sun ISI -1.7512871885
## 1615 jul tue temp -0.2083578670
## 1616 sep tue temp 0.3904829792
## 1617 sep mon temp 0.4228527546
## 1618 aug wed temp 0.6494411829
## 1619 aug fri temp 0.3095585405
## 1620 jul sat temp -0.4349462953
## 1621 aug wed temp 0.7303656216
## 1622 aug thu temp 1.3130215800
## 1623 mar mon temp -0.9852324782
## 1624 sep tue temp 0.7951051725
## 1625 aug tue temp -0.3054671934
## 1626 sep thu temp 0.7141807338
## 1627 jun fri temp 0.6332562952
## 1628 jul sun temp 0.8922144989
## 1629 jul sat temp 0.8598447234
## 1630 sep fri temp 0.1315247754
## 1631 sep sat temp 1.6690891101
## 1632 aug sun temp -0.4673160707
## 1633 sep sat temp 1.5072402328
## 1634 aug wed temp -0.1436183161
## 1635 aug wed temp 0.1962643263
## 1636 sep fri temp -0.0465089897
## 1637 mar mon temp -0.5158707339
## 1638 aug thu temp 0.1638945509
## 1639 mar sat temp -0.6615347235
## 1640 sep sat temp -0.2407276425
## 1641 sep sun temp -0.2407276425
## 1642 mar thu temp -2.2638386092
## 1643 aug wed temp -0.4349462953
## 1644 aug wed temp 0.6656260706
## 1645 mar fri temp -0.7586440499
## 1646 aug thu temp 0.2286341018
## 1647 sep wed temp 0.4228527546
## 1648 aug wed temp -0.3054671934
## 1649 aug sun temp 0.1315247754
## 1650 sep mon temp -0.2569125302
## 1651 aug sat temp -0.8233836009
## 1652 aug sat temp 0.1638945509
## 1653 apr thu temp -2.1829141705
## 1654 aug sun temp -0.0141392142
## 1655 sep wed temp -0.1598032038
## 1656 aug tue temp -0.7910138254
## 1657 sep sun temp 0.7465505093
## 1658 oct mon temp -0.0303241019
## 1659 feb sun temp -1.1147115801
## 1660 oct mon temp -0.4025765198
## 1661 aug fri temp 0.2448189895
## 1662 sep tue temp -0.2730974179
## 1663 mar sun temp -1.2603755697
## 1664 sep mon temp 0.2771887650
## 1665 mar sat temp -0.9690475905
## 1666 mar sun temp -1.2603755697
## 1667 mar fri temp -1.2280057942
## 1668 aug thu temp 0.7951051725
## 1669 aug tue temp 0.8598447234
## 1670 sep wed temp 0.8112900602
## 1671 aug tue temp 0.8598447234
## 1672 aug fri temp 0.6818109584
## 1673 apr thu temp -2.1829141705
## 1674 sep thu temp 0.3581132037
## 1675 sep tue temp -0.8719382641
## 1676 sep mon temp 0.5361469688
## 1677 sep tue temp 0.3742980914
## 1678 mar sun temp -1.1147115801
## 1679 feb sun temp -1.6973675385
## 1680 oct wed temp 0.1477096631
## 1681 mar sat temp -0.6777196113
## 1682 sep thu temp 0.4552225301
## 1683 aug sat temp 0.5847016320
## 1684 sep tue temp 0.2286341018
## 1685 sep fri temp 0.0506003367
## 1686 sep thu temp 0.6332562952
## 1687 oct sat temp -0.1436183161
## 1688 aug sat temp -2.2962083847
## 1689 sep fri temp 0.1315247754
## 1690 mar mon temp -1.3413000084
## 1691 mar sat temp -0.3702067443
## 1692 mar sat temp -0.3702067443
## 1693 sep sun temp -0.3863916321
## 1694 sep mon temp -1.1147115801
## 1695 sep wed temp 0.0182305613
## 1696 mar mon temp -0.6615347235
## 1697 aug sun temp -0.4996858462
## 1698 sep fri temp -0.1112485406
## 1699 mar mon temp -1.3413000084
## 1700 jul fri temp -0.9528627028
## 1701 sep wed temp -0.6291649481
## 1702 sep sun temp 0.5847016320
## 1703 oct mon temp -0.5158707339
## 1704 aug sat temp 0.1315247754
## 1705 sep sun temp 1.4586855696
## 1706 aug sat temp -0.4673160707
## 1707 sep wed temp 1.1511727026
## 1708 sep sun temp 1.3777611309
## 1709 sep tue temp -0.0950636529
## 1710 sep tue temp 0.8112900602
## 1711 sep sat temp -0.2569125302
## 1712 aug sun temp 0.0506003367
## 1713 sep sat temp -0.1759880915
## 1714 sep tue temp -0.0788787651
## 1715 sep sat temp 0.9407691621
## 1716 aug sun temp -0.6291649481
## 1717 aug sun temp 0.5037771933
## 1718 aug sun temp 1.2159122536
## 1719 aug wed temp 0.2286341018
## 1720 aug wed temp 0.3904829792
## 1721 aug wed temp 1.2159122536
## 1722 aug wed temp 0.7627353970
## 1723 aug wed temp 0.4552225301
## 1724 aug thu temp 0.3419283160
## 1725 aug thu temp -0.0626938774
## 1726 aug sat temp 0.7465505093
## 1727 aug sat temp 0.3419283160
## 1728 aug sat temp 0.7141807338
## 1729 aug mon temp 1.4586855696
## 1730 aug fri temp -1.3089302329
## 1731 aug fri temp 0.3419283160
## 1732 aug fri temp 0.0020456735
## 1733 aug fri temp 0.4066678669
## 1734 aug tue temp 0.4552225301
## 1735 aug tue temp 0.0182305613
## 1736 aug tue temp -0.0303241019
## 1737 aug tue temp 0.4066678669
## 1738 aug tue temp 0.1315247754
## 1739 aug tue temp 0.1477096631
## 1740 dec sun temp -2.3447630479
## 1741 dec wed temp -2.2962083847
## 1742 dec thu temp -2.2962083847
## 1743 dec mon temp -2.3771328233
## 1744 dec mon temp -2.3771328233
## 1745 dec mon temp -2.3771328233
## 1746 dec mon temp -2.3771328233
## 1747 dec fri temp -2.7655701289
## 1748 dec tue temp -2.2962083847
## 1749 feb wed temp -1.6973675385
## 1750 feb fri temp -1.9077710790
## 1751 jul sat temp 0.9731389376
## 1752 jul fri temp 0.5847016320
## 1753 jul tue temp 1.2320971413
## 1754 jul tue temp -0.3540218566
## 1755 jun sun temp -0.8071987131
## 1756 jun mon temp -0.0303241019
## 1757 sep sun temp -0.3540218566
## 1758 sep sun temp 0.7303656216
## 1759 sep sun temp 0.8922144989
## 1760 sep wed temp 0.0667852245
## 1761 sep thu temp -0.0141392142
## 1762 sep thu temp 0.3742980914
## 1763 sep thu temp -0.0626938774
## 1764 sep thu temp -0.4025765198
## 1765 sep thu temp -0.4025765198
## 1766 sep thu temp -1.0337871414
## 1767 sep thu temp -0.9043080396
## 1768 sep sat temp 0.6818109584
## 1769 sep sat temp 0.7951051725
## 1770 sep sat temp 0.3581132037
## 1771 sep sat temp -0.3540218566
## 1772 sep mon temp -0.1921729793
## 1773 sep mon temp 0.6008865197
## 1774 sep mon temp 0.5523318565
## 1775 sep mon temp 0.1800794386
## 1776 sep mon temp 0.0020456735
## 1777 sep mon temp -0.5482405094
## 1778 sep mon temp -1.1470813556
## 1779 sep mon temp -0.4025765198
## 1780 sep mon temp 0.3257434282
## 1781 sep fri temp -1.0499720292
## 1782 sep fri temp -1.4869639980
## 1783 sep fri temp -0.6291649481
## 1784 sep fri temp 0.2124492141
## 1785 sep fri temp 0.0829701122
## 1786 sep fri temp -0.0950636529
## 1787 sep fri temp 0.2448189895
## 1788 sep fri temp 0.2448189895
## 1789 sep fri temp 0.2933736528
## 1790 sep fri temp -0.1436183161
## 1791 sep fri temp -0.3216520811
## 1792 sep fri temp -0.6615347235
## 1793 sep tue temp -0.5482405094
## 1794 sep tue temp 0.2933736528
## 1795 sep tue temp 0.0506003367
## 1796 sep tue temp -0.5482405094
## 1797 sep sat temp -0.4025765198
## 1798 sep sun temp -0.8881231518
## 1799 sep fri temp -1.4545942225
## 1800 sep sat temp -0.6291649481
## 1801 aug sat temp 0.4228527546
## 1802 jul wed temp 0.0020456735
## 1803 aug thu temp -0.4996858462
## 1804 aug wed temp 1.4425006819
## 1805 aug thu temp 0.1962643263
## 1806 aug sat temp 0.3257434282
## 1807 aug sun temp 0.2610038773
## 1808 sep sun temp 0.2124492141
## 1809 aug fri temp 0.6494411829
## 1810 feb mon temp -1.9077710790
## 1811 sep fri temp 0.2286341018
## 1812 sep sun temp 0.4228527546
## 1813 feb sun temp -1.4869639980
## 1814 sep sun temp 0.1800794386
## 1815 aug sun temp 0.8112900602
## 1816 jun wed temp 1.4101309064
## 1817 sep thu temp 0.5685167442
## 1818 sep wed temp 0.4066678669
## 1819 sep sat temp -0.3702067443
## 1820 sep fri temp 0.0991549999
## 1821 feb fri temp -1.7944768649
## 1822 jul mon temp 0.5685167442
## 1823 aug thu temp 1.3292064677
## 1824 jul tue temp 1.1349878149
## 1825 aug sun temp 0.9083993866
## 1826 aug sun temp 0.8922144989
## 1827 aug wed temp 1.1188029272
## 1828 jul sun temp 1.6205344469
## 1829 sep sat temp 0.4875923056
## 1830 aug sat temp 1.2320971413
## 1831 aug mon temp 1.3939460187
## 1832 aug sun temp 0.8598447234
## 1833 aug sat temp 0.6494411829
## 1834 aug sun temp 0.2610038773
## 1835 aug mon temp 1.2159122536
## 1836 aug sat temp 0.6979958461
## 1837 sep fri temp -0.0303241019
## 1838 aug mon temp 1.0055087130
## 1839 apr mon temp -1.3574848961
## 1840 sep fri temp -0.4996858462
## 1841 aug wed temp 0.6656260706
## 1842 aug fri temp -1.2118209065
## 1843 aug wed temp 0.0991549999
## 1844 aug sat temp -0.0626938774
## 1845 aug sat temp -0.6129800603
## 1846 sep sun temp -0.7748289377
## 1847 feb tue temp -2.3771328233
## 1848 feb tue temp -2.2962083847
## 1849 feb sat temp -2.3771328233
## 1850 mar mon temp -1.4707791102
## 1851 mar wed temp -1.3089302329
## 1852 mar thu temp -0.9690475905
## 1853 apr sun temp -0.9043080396
## 1854 may fri temp -0.2083578670
## 1855 jun mon temp -0.8071987131
## 1856 jun sat temp 0.8436598357
## 1857 jun thu temp 1.1511727026
## 1858 jun thu temp 0.5523318565
## 1859 jul thu temp 1.2806518045
## 1860 jul sun temp 1.1026180394
## 1861 jul sun temp -0.1759880915
## 1862 jul mon temp 0.5361469688
## 1863 jul thu temp 1.7661984365
## 1864 aug sun temp 0.6656260706
## 1865 aug sun temp 2.2355601808
## 1866 aug mon temp 1.8309379875
## 1867 aug tue temp 0.7789202848
## 1868 aug tue temp 1.1511727026
## 1869 aug tue temp 0.0182305613
## 1870 aug fri temp 1.3292064677
## 1871 aug sat temp 1.8633077629
## 1872 aug mon temp 2.1546357421
## 1873 aug tue temp 2.1060810789
## 1874 aug tue temp 2.2679299563
## 1875 aug tue temp 1.2968366922
## 1876 aug wed temp 1.6043495592
## 1877 aug wed temp 1.5557948960
## 1878 aug thu temp 1.1997273658
## 1879 aug fri temp 0.2933736528
## 1880 aug fri temp -0.1759880915
## 1881 aug sun temp 1.3777611309
## 1882 aug sun temp 0.4228527546
## 1883 aug sun temp 0.3095585405
## 1884 jul tue RH -0.1159142113
## 1885 sep tue RH -0.3807203501
## 1886 sep mon RH -0.3145188154
## 1887 aug wed RH -0.8441310930
## 1888 aug fri RH 0.4798996010
## 1889 jul sat RH 0.6123026703
## 1890 aug wed RH -0.7779295583
## 1891 aug thu RH -1.4399449052
## 1892 mar mon RH -0.2483172807
## 1893 sep tue RH -1.0427356971
## 1894 aug tue RH -0.0497126766
## 1895 sep thu RH -1.2413403011
## 1896 jun fri RH -0.3145188154
## 1897 jul sun RH -0.9765341624
## 1898 jul sat RH -0.0497126766
## 1899 sep fri RH 0.2150934622
## 1900 sep sat RH -1.1089372317
## 1901 aug sun RH 0.2150934622
## 1902 sep sat RH -1.1089372317
## 1903 aug wed RH 0.0826903928
## 1904 aug wed RH -0.5793249542
## 1905 sep fri RH -0.6455264889
## 1906 mar mon RH -0.9765341624
## 1907 aug thu RH -0.1821157460
## 1908 mar sat RH -0.8441310930
## 1909 sep sat RH 0.8109072744
## 1910 sep sun RH 1.5391241561
## 1911 mar thu RH 1.7377287602
## 1912 aug wed RH 0.2150934622
## 1913 aug wed RH -0.7117280236
## 1914 mar fri RH -1.1751387664
## 1915 aug thu RH 0.0826903928
## 1916 sep wed RH -0.5793249542
## 1917 aug wed RH 0.4136980663
## 1918 aug sun RH -0.3145188154
## 1919 sep mon RH -0.3145188154
## 1920 aug sat RH 0.6123026703
## 1921 aug sat RH -0.3145188154
## 1922 apr thu RH 0.6785042050
## 1923 aug sun RH 0.0164888581
## 1924 sep wed RH 0.0826903928
## 1925 aug tue RH 1.4729226214
## 1926 sep sun RH -0.7779295583
## 1927 oct mon RH -0.7779295583
## 1928 feb sun RH 0.6123026703
## 1929 oct mon RH 0.0826903928
## 1930 aug fri RH -0.6455264889
## 1931 sep tue RH 0.1488919275
## 1932 mar sun RH -0.3145188154
## 1933 sep mon RH -0.1159142113
## 1934 mar sat RH -0.1159142113
## 1935 mar sun RH 1.0757134132
## 1936 mar fri RH -0.7117280236
## 1937 aug thu RH -1.0427356971
## 1938 aug tue RH -1.4399449052
## 1939 sep wed RH -1.2413403011
## 1940 aug tue RH -1.4399449052
## 1941 aug fri RH -0.5131234195
## 1942 apr thu RH 0.6785042050
## 1943 sep thu RH -1.9033556481
## 1944 sep tue RH 1.0095118785
## 1945 sep mon RH -0.3807203501
## 1946 sep tue RH -0.7117280236
## 1947 mar sun RH 0.6785042050
## 1948 feb sun RH 1.6053256908
## 1949 oct wed RH -0.4469218848
## 1950 mar sat RH 1.3405195520
## 1951 sep thu RH -0.6455264889
## 1952 aug sat RH -0.8441310930
## 1953 sep tue RH -0.4469218848
## 1954 sep fri RH -0.7117280236
## 1955 sep thu RH -1.1751387664
## 1956 oct sat RH -1.2413403011
## 1957 aug sat RH 3.4589686623
## 1958 sep fri RH 0.2150934622
## 1959 mar mon RH 0.1488919275
## 1960 mar sat RH -1.1089372317
## 1961 mar sat RH -1.1089372317
## 1962 sep sun RH 1.0757134132
## 1963 sep mon RH 1.9363333643
## 1964 sep wed RH -1.6385495093
## 1965 mar mon RH -1.1089372317
## 1966 aug sun RH 1.0095118785
## 1967 sep fri RH 0.3474965316
## 1968 mar mon RH 0.1488919275
## 1969 jul fri RH 2.3335425724
## 1970 sep wed RH 0.8771088091
## 1971 sep sun RH -0.3145188154
## 1972 oct mon RH 0.0164888581
## 1973 aug sat RH -0.6455264889
## 1974 sep sun RH -1.1751387664
## 1975 aug sat RH -0.0497126766
## 1976 sep wed RH -1.5061464399
## 1977 sep sun RH -1.1089372317
## 1978 sep tue RH -0.0497126766
## 1979 sep tue RH -0.5131234195
## 1980 sep sat RH -1.2413403011
## 1981 aug sun RH -0.1821157460
## 1982 sep sat RH 0.1488919275
## 1983 sep tue RH -0.2483172807
## 1984 sep sat RH -1.1089372317
## 1985 aug sun RH 1.4729226214
## 1986 aug sun RH 0.6785042050
## 1987 aug sun RH -0.3807203501
## 1988 aug wed RH 1.7377287602
## 1989 aug wed RH -0.2483172807
## 1990 aug wed RH -1.2413403011
## 1991 aug wed RH -0.5131234195
## 1992 aug wed RH -0.4469218848
## 1993 aug thu RH -0.3807203501
## 1994 aug thu RH -0.1821157460
## 1995 aug sat RH -0.1821157460
## 1996 aug sat RH 0.0164888581
## 1997 aug sat RH -0.2483172807
## 1998 aug mon RH -0.7779295583
## 1999 aug fri RH 2.6645502459
## 2000 aug fri RH -0.1159142113
## 2001 aug fri RH -0.3145188154
## 2002 aug fri RH 0.6123026703
## 2003 aug tue RH 0.6785042050
## 2004 aug tue RH 0.7447057397
## 2005 aug tue RH 0.6123026703
## 2006 aug tue RH 0.8109072744
## 2007 aug tue RH 0.9433103438
## 2008 aug tue RH 0.2150934622
## 2009 dec sun RH 0.8771088091
## 2010 dec wed RH 1.1419149479
## 2011 dec thu RH 1.1419149479
## 2012 dec mon RH -1.5061464399
## 2013 dec mon RH -1.5061464399
## 2014 dec mon RH -1.5061464399
## 2015 dec mon RH -1.5061464399
## 2016 dec fri RH 1.0095118785
## 2017 dec tue RH -1.3075418358
## 2018 feb wed RH -0.5793249542
## 2019 feb fri RH 0.1488919275
## 2020 jul sat RH -0.3145188154
## 2021 jul fri RH -0.2483172807
## 2022 jul tue RH -1.0427356971
## 2023 jul tue RH 1.5391241561
## 2024 jun sun RH 0.1488919275
## 2025 jun mon RH -0.3145188154
## 2026 sep sun RH 0.6123026703
## 2027 sep sun RH -0.5793249542
## 2028 sep sun RH -1.0427356971
## 2029 sep wed RH -0.1821157460
## 2030 sep thu RH -1.3075418358
## 2031 sep thu RH -1.0427356971
## 2032 sep thu RH -0.6455264889
## 2033 sep thu RH -1.0427356971
## 2034 sep thu RH -1.0427356971
## 2035 sep thu RH -0.3145188154
## 2036 sep thu RH 0.8109072744
## 2037 sep sat RH -1.1089372317
## 2038 sep sat RH -1.1089372317
## 2039 sep sat RH -1.0427356971
## 2040 sep sat RH -0.1821157460
## 2041 sep mon RH 0.6785042050
## 2042 sep mon RH -0.6455264889
## 2043 sep mon RH -0.5793249542
## 2044 sep mon RH -0.1821157460
## 2045 sep mon RH 0.0164888581
## 2046 sep mon RH 0.4798996010
## 2047 sep mon RH 1.4729226214
## 2048 sep mon RH -0.0497126766
## 2049 sep mon RH -0.5793249542
## 2050 sep fri RH 1.3405195520
## 2051 sep fri RH 2.0687364337
## 2052 sep fri RH 0.6123026703
## 2053 sep fri RH -0.0497126766
## 2054 sep fri RH 0.2150934622
## 2055 sep fri RH 0.4136980663
## 2056 sep fri RH -0.5793249542
## 2057 sep fri RH -0.5793249542
## 2058 sep fri RH -0.3145188154
## 2059 sep fri RH -0.1159142113
## 2060 sep fri RH 0.0826903928
## 2061 sep fri RH 1.3405195520
## 2062 sep tue RH 0.6123026703
## 2063 sep tue RH -0.5793249542
## 2064 sep tue RH 0.0826903928
## 2065 sep tue RH -0.3807203501
## 2066 sep sat RH 0.2150934622
## 2067 sep sun RH 2.2011395031
## 2068 sep fri RH 2.2673410377
## 2069 sep sat RH 0.8771088091
## 2070 aug sat RH -0.1159142113
## 2071 jul wed RH -0.3145188154
## 2072 aug thu RH 1.2743180173
## 2073 aug wed RH -0.9765341624
## 2074 aug thu RH 0.9433103438
## 2075 aug sat RH 0.0164888581
## 2076 aug sun RH 0.4136980663
## 2077 sep sun RH 0.7447057397
## 2078 aug fri RH -0.6455264889
## 2079 feb mon RH 1.8039302949
## 2080 sep fri RH 0.1488919275
## 2081 sep sun RH -0.0497126766
## 2082 feb sun RH 1.2081164826
## 2083 sep sun RH 0.7447057397
## 2084 aug sun RH -0.7117280236
## 2085 jun wed RH -0.6455264889
## 2086 sep thu RH 0.1488919275
## 2087 sep wed RH -0.6455264889
## 2088 sep sat RH 1.5391241561
## 2089 sep fri RH 0.0164888581
## 2090 feb fri RH 0.6123026703
## 2091 jul mon RH -1.1089372317
## 2092 aug thu RH -1.1089372317
## 2093 jul tue RH -0.3145188154
## 2094 aug sun RH -0.1159142113
## 2095 aug sun RH -0.5131234195
## 2096 aug wed RH -0.5131234195
## 2097 jul sun RH -1.1089372317
## 2098 sep sat RH 0.2812949969
## 2099 aug sat RH -0.8441310930
## 2100 aug mon RH -0.7117280236
## 2101 aug sun RH 0.0164888581
## 2102 aug sat RH -0.2483172807
## 2103 aug sun RH 1.4729226214
## 2104 aug mon RH -0.5793249542
## 2105 aug sat RH 0.6123026703
## 2106 sep fri RH 0.1488919275
## 2107 aug mon RH -0.9765341624
## 2108 apr mon RH 1.3405195520
## 2109 sep fri RH 0.9433103438
## 2110 aug wed RH 0.3474965316
## 2111 aug fri RH 2.9293563847
## 2112 aug wed RH 0.4136980663
## 2113 aug sat RH 1.3405195520
## 2114 aug sat RH 1.8701318296
## 2115 sep sun RH 2.1349379684
## 2116 feb tue RH 2.5321471765
## 2117 feb tue RH 2.2011395031
## 2118 feb sat RH 1.0095118785
## 2119 mar mon RH 0.0826903928
## 2120 mar wed RH -0.1821157460
## 2121 mar thu RH -1.1089372317
## 2122 apr sun RH -0.7117280236
## 2123 may fri RH -0.2483172807
## 2124 jun mon RH 2.3335425724
## 2125 jun sat RH 0.4136980663
## 2126 jun thu RH -0.5793249542
## 2127 jun thu RH -0.2483172807
## 2128 jul thu RH -1.0427356971
## 2129 jul sun RH 0.0826903928
## 2130 jul sun RH 2.5321471765
## 2131 jul mon RH 0.8771088091
## 2132 jul thu RH -1.2413403011
## 2133 aug sun RH -0.2483172807
## 2134 aug sun RH -1.2413403011
## 2135 aug mon RH -1.0427356971
## 2136 aug tue RH -0.0497126766
## 2137 aug tue RH -0.6455264889
## 2138 aug tue RH 1.8039302949
## 2139 aug fri RH -0.9765341624
## 2140 aug sat RH -0.9103326277
## 2141 aug mon RH -1.1751387664
## 2142 aug tue RH -1.1089372317
## 2143 aug tue RH -1.1751387664
## 2144 aug tue RH 1.2743180173
## 2145 aug wed RH -0.9103326277
## 2146 aug wed RH -0.9765341624
## 2147 aug thu RH -0.5793249542
## 2148 aug fri RH 1.8039302949
## 2149 aug fri RH 1.2081164826
## 2150 aug sun RH -0.7779295583
## 2151 aug sun RH 1.8039302949
## 2152 aug sun RH 1.7377287602
## 2153 jul tue wind -0.7464731398
## 2154 sep tue wind -1.0115223408
## 2155 sep mon wind -1.2235617015
## 2156 aug wed wind 0.2077039835
## 2157 aug fri wind 2.5401369516
## 2158 jul sat wind 0.6847925451
## 2159 aug wed wind 0.6847925451
## 2160 aug thu wind -0.0573452174
## 2161 mar mon wind 0.6847925451
## 2162 sep tue wind -0.2693845782
## 2163 aug tue wind 1.3739204675
## 2164 sep thu wind 0.2077039835
## 2165 jun fri wind 0.6847925451
## 2166 jul sun wind -1.0115223408
## 2167 jul sat wind -1.2235617015
## 2168 sep fri wind 0.4197433442
## 2169 sep sat wind -0.7464731398
## 2170 aug sun wind -1.4886109024
## 2171 sep sat wind -1.0115223408
## 2172 aug wed wind -0.2693845782
## 2173 aug wed wind -0.0573452174
## 2174 sep fri wind 0.8968319059
## 2175 mar mon wind -0.5344337791
## 2176 aug thu wind -0.0573452174
## 2177 mar sat wind 2.3280975908
## 2178 sep sat wind -1.2235617015
## 2179 sep sun wind -1.0115223408
## 2180 mar thu wind 0.2077039835
## 2181 aug wed wind -1.7006502631
## 2182 aug wed wind 0.2077039835
## 2183 mar fri wind 2.8051861525
## 2184 aug thu wind -1.0115223408
## 2185 sep wed wind -1.2235617015
## 2186 aug wed wind -0.0573452174
## 2187 aug sun wind 0.6847925451
## 2188 sep mon wind -1.0115223408
## 2189 aug sat wind -1.2235617015
## 2190 aug sat wind 0.4197433442
## 2191 apr thu wind 0.8968319059
## 2192 aug sun wind -0.7464731398
## 2193 sep wed wind -1.0115223408
## 2194 aug tue wind 0.6847925451
## 2195 sep sun wind 1.3739204675
## 2196 oct mon wind -0.0573452174
## 2197 feb sun wind -1.0115223408
## 2198 oct mon wind 0.2077039835
## 2199 aug fri wind 0.4197433442
## 2200 sep tue wind -0.5344337791
## 2201 mar sun wind 0.8968319059
## 2202 sep mon wind -1.0115223408
## 2203 mar sat wind -1.7006502631
## 2204 mar sun wind -0.0573452174
## 2205 mar fri wind -0.0573452174
## 2206 aug thu wind -0.7464731398
## 2207 aug tue wind 0.2077039835
## 2208 sep wed wind -0.0573452174
## 2209 aug tue wind 0.2077039835
## 2210 aug fri wind 0.6847925451
## 2211 apr thu wind 0.8968319059
## 2212 sep thu wind -1.7006502631
## 2213 sep tue wind 1.1618811068
## 2214 sep mon wind -0.2693845782
## 2215 sep tue wind -1.0115223408
## 2216 mar sun wind -0.2693845782
## 2217 feb sun wind -1.0115223408
## 2218 oct wed wind -0.7464731398
## 2219 mar sat wind -0.0573452174
## 2220 sep thu wind -1.2235617015
## 2221 aug sat wind 1.6389696684
## 2222 sep tue wind -1.0115223408
## 2223 sep fri wind 1.1618811068
## 2224 sep thu wind 0.4197433442
## 2225 oct sat wind -0.5344337791
## 2226 aug sat wind 0.8968319059
## 2227 sep fri wind 0.4197433442
## 2228 mar mon wind 0.8968319059
## 2229 mar sat wind 0.4197433442
## 2230 mar sat wind 0.4197433442
## 2231 sep sun wind -1.4886109024
## 2232 sep mon wind 1.1618811068
## 2233 sep wed wind -1.4886109024
## 2234 mar mon wind -0.5344337791
## 2235 aug sun wind -0.5344337791
## 2236 sep fri wind -0.2693845782
## 2237 mar mon wind 0.8968319059
## 2238 jul fri wind -0.2693845782
## 2239 sep wed wind 0.2077039835
## 2240 sep sun wind 0.4197433442
## 2241 oct mon wind -0.0573452174
## 2242 aug sat wind 0.2077039835
## 2243 sep sun wind -0.5344337791
## 2244 aug sat wind -0.0573452174
## 2245 sep wed wind 0.2077039835
## 2246 sep sun wind -0.5344337791
## 2247 sep tue wind -0.7464731398
## 2248 sep tue wind -0.5344337791
## 2249 sep sat wind -0.5344337791
## 2250 aug sun wind 0.8968319059
## 2251 sep sat wind -1.2235617015
## 2252 sep tue wind -1.0115223408
## 2253 sep sat wind -0.0573452174
## 2254 aug sun wind -0.0573452174
## 2255 aug sun wind 1.8510090292
## 2256 aug sun wind 1.1618811068
## 2257 aug wed wind -1.0115223408
## 2258 aug wed wind -1.9656994641
## 2259 aug wed wind -0.5344337791
## 2260 aug wed wind -0.5344337791
## 2261 aug wed wind -0.2693845782
## 2262 aug thu wind -0.7464731398
## 2263 aug thu wind -0.5344337791
## 2264 aug sat wind -1.0115223408
## 2265 aug sat wind -0.7464731398
## 2266 aug sat wind -1.2235617015
## 2267 aug mon wind -0.0573452174
## 2268 aug fri wind 1.8510090292
## 2269 aug fri wind -0.5344337791
## 2270 aug fri wind -0.2693845782
## 2271 aug fri wind -0.5344337791
## 2272 aug tue wind 1.8510090292
## 2273 aug tue wind -0.0573452174
## 2274 aug tue wind -0.7464731398
## 2275 aug tue wind -0.5344337791
## 2276 aug tue wind 0.2077039835
## 2277 aug tue wind -0.0573452174
## 2278 dec sun wind 2.3280975908
## 2279 dec wed wind 2.0630483899
## 2280 dec thu wind 0.4197433442
## 2281 dec mon wind 2.3280975908
## 2282 dec mon wind 2.3280975908
## 2283 dec mon wind 2.3280975908
## 2284 dec mon wind 2.3280975908
## 2285 dec fri wind 0.4197433442
## 2286 dec tue wind 2.3280975908
## 2287 feb wed wind -0.5344337791
## 2288 feb fri wind 2.0630483899
## 2289 jul sat wind -1.7006502631
## 2290 jul fri wind -1.4886109024
## 2291 jul tue wind 0.6847925451
## 2292 jul tue wind -0.2693845782
## 2293 jun sun wind -1.2235617015
## 2294 jun mon wind 0.6847925451
## 2295 sep sun wind 0.6847925451
## 2296 sep sun wind -0.2693845782
## 2297 sep sun wind -1.2235617015
## 2298 sep wed wind -1.2235617015
## 2299 sep thu wind 0.4197433442
## 2300 sep thu wind 1.1618811068
## 2301 sep thu wind 1.6389696684
## 2302 sep thu wind -0.0573452174
## 2303 sep thu wind -0.0573452174
## 2304 sep thu wind -0.7464731398
## 2305 sep thu wind -1.2235617015
## 2306 sep sat wind -0.0573452174
## 2307 sep sat wind -0.5344337791
## 2308 sep sat wind 0.2077039835
## 2309 sep sat wind -1.0115223408
## 2310 sep mon wind -0.5344337791
## 2311 sep mon wind -1.0115223408
## 2312 sep mon wind -1.0115223408
## 2313 sep mon wind -1.2235617015
## 2314 sep mon wind -1.0115223408
## 2315 sep mon wind 0.2077039835
## 2316 sep mon wind 0.4197433442
## 2317 sep mon wind -0.5344337791
## 2318 sep mon wind -1.0115223408
## 2319 sep fri wind -0.2693845782
## 2320 sep fri wind -0.2693845782
## 2321 sep fri wind 1.1618811068
## 2322 sep fri wind -0.2693845782
## 2323 sep fri wind -0.7464731398
## 2324 sep fri wind -1.0115223408
## 2325 sep fri wind 0.4197433442
## 2326 sep fri wind 0.4197433442
## 2327 sep fri wind -1.0115223408
## 2328 sep fri wind -1.0115223408
## 2329 sep fri wind -0.0573452174
## 2330 sep fri wind -0.5344337791
## 2331 sep tue wind -1.0115223408
## 2332 sep tue wind -0.7464731398
## 2333 sep tue wind -0.5344337791
## 2334 sep tue wind 0.6847925451
## 2335 sep sat wind 0.4197433442
## 2336 sep sun wind 1.8510090292
## 2337 sep fri wind -0.0573452174
## 2338 sep sat wind 0.4197433442
## 2339 aug sat wind -1.0115223408
## 2340 jul wed wind 1.6389696684
## 2341 aug thu wind -0.7464731398
## 2342 aug wed wind -1.2235617015
## 2343 aug thu wind -0.7464731398
## 2344 aug sat wind 0.2077039835
## 2345 aug sun wind -1.0115223408
## 2346 sep sun wind 0.6847925451
## 2347 aug fri wind -0.5344337791
## 2348 feb mon wind 1.1618811068
## 2349 sep fri wind -0.7464731398
## 2350 sep sun wind -0.0573452174
## 2351 feb sun wind -1.2235617015
## 2352 sep sun wind 0.4197433442
## 2353 aug sun wind -0.2693845782
## 2354 jun wed wind 0.2077039835
## 2355 sep thu wind -0.0573452174
## 2356 sep wed wind -1.0115223408
## 2357 sep sat wind 0.4197433442
## 2358 sep fri wind -0.5344337791
## 2359 feb fri wind 2.8051861525
## 2360 jul mon wind 0.2077039835
## 2361 aug thu wind 0.4197433442
## 2362 jul tue wind -0.5344337791
## 2363 aug sun wind 0.6847925451
## 2364 aug sun wind -0.0573452174
## 2365 aug wed wind 0.2077039835
## 2366 jul sun wind -0.2693845782
## 2367 sep sat wind -0.0573452174
## 2368 aug sat wind -0.2693845782
## 2369 aug mon wind -1.0115223408
## 2370 aug sun wind -0.0573452174
## 2371 aug sat wind -0.0573452174
## 2372 aug sun wind 0.4197433442
## 2373 aug mon wind -1.4886109024
## 2374 aug sat wind -0.0573452174
## 2375 sep fri wind -1.0115223408
## 2376 aug mon wind -1.2235617015
## 2377 apr mon wind -0.5344337791
## 2378 sep fri wind -0.2693845782
## 2379 aug wed wind 0.6847925451
## 2380 aug fri wind 0.4197433442
## 2381 aug wed wind -0.0573452174
## 2382 aug sat wind 0.4197433442
## 2383 aug sat wind 2.0630483899
## 2384 sep sun wind 1.8510090292
## 2385 feb tue wind 1.1618811068
## 2386 feb tue wind 0.6847925451
## 2387 feb sat wind -1.7006502631
## 2388 mar mon wind 0.8968319059
## 2389 mar wed wind 0.6847925451
## 2390 mar thu wind -0.2693845782
## 2391 apr sun wind 2.8051861525
## 2392 may fri wind -0.0573452174
## 2393 jun mon wind -0.0573452174
## 2394 jun sat wind -0.5344337791
## 2395 jun thu wind -0.7464731398
## 2396 jun thu wind 2.8051861525
## 2397 jul thu wind -1.4886109024
## 2398 jul sun wind -0.0573452174
## 2399 jul sun wind 0.2077039835
## 2400 jul mon wind 0.4197433442
## 2401 jul thu wind 0.2077039835
## 2402 aug sun wind 0.8968319059
## 2403 aug sun wind -0.0573452174
## 2404 aug mon wind -0.2693845782
## 2405 aug tue wind 1.1618811068
## 2406 aug tue wind -0.2693845782
## 2407 aug tue wind 1.8510090292
## 2408 aug fri wind 0.2077039835
## 2409 aug sat wind 0.4197433442
## 2410 aug mon wind -0.5344337791
## 2411 aug tue wind -1.0115223408
## 2412 aug tue wind -0.7464731398
## 2413 aug tue wind 0.4197433442
## 2414 aug wed wind 0.4197433442
## 2415 aug wed wind 0.4197433442
## 2416 aug thu wind -1.2235617015
## 2417 aug fri wind 1.8510090292
## 2418 aug fri wind 0.6847925451
## 2419 aug sun wind -0.7464731398
## 2420 aug sun wind 0.8968319059
## 2421 aug sun wind 1.3739204675
## 2422 jul tue rain -0.0726485861
## 2423 sep tue rain -0.0726485861
## 2424 sep mon rain -0.0726485861
## 2425 aug wed rain -0.0726485861
## 2426 aug fri rain -0.0726485861
## 2427 jul sat rain -0.0726485861
## 2428 aug wed rain -0.0726485861
## 2429 aug thu rain -0.0726485861
## 2430 mar mon rain -0.0726485861
## 2431 sep tue rain -0.0726485861
## 2432 aug tue rain -0.0726485861
## 2433 sep thu rain -0.0726485861
## 2434 jun fri rain -0.0726485861
## 2435 jul sun rain -0.0726485861
## 2436 jul sat rain -0.0726485861
## 2437 sep fri rain -0.0726485861
## 2438 sep sat rain -0.0726485861
## 2439 aug sun rain -0.0726485861
## 2440 sep sat rain -0.0726485861
## 2441 aug wed rain -0.0726485861
## 2442 aug wed rain -0.0726485861
## 2443 sep fri rain -0.0726485861
## 2444 mar mon rain -0.0726485861
## 2445 aug thu rain -0.0726485861
## 2446 mar sat rain -0.0726485861
## 2447 sep sat rain -0.0726485861
## 2448 sep sun rain -0.0726485861
## 2449 mar thu rain -0.0726485861
## 2450 aug wed rain -0.0726485861
## 2451 aug wed rain -0.0726485861
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## 2453 aug thu rain -0.0726485861
## 2454 sep wed rain -0.0726485861
## 2455 aug wed rain -0.0726485861
## 2456 aug sun rain -0.0726485861
## 2457 sep mon rain -0.0726485861
## 2458 aug sat rain -0.0726485861
## 2459 aug sat rain -0.0726485861
## 2460 apr thu rain -0.0726485861
## 2461 aug sun rain -0.0726485861
## 2462 sep wed rain -0.0726485861
## 2463 aug tue rain -0.0726485861
## 2464 sep sun rain -0.0726485861
## 2465 oct mon rain -0.0726485861
## 2466 feb sun rain -0.0726485861
## 2467 oct mon rain -0.0726485861
## 2468 aug fri rain -0.0726485861
## 2469 sep tue rain -0.0726485861
## 2470 mar sun rain -0.0726485861
## 2471 sep mon rain -0.0726485861
## 2472 mar sat rain -0.0726485861
## 2473 mar sun rain -0.0726485861
## 2474 mar fri rain -0.0726485861
## 2475 aug thu rain -0.0726485861
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## 2477 sep wed rain -0.0726485861
## 2478 aug tue rain -0.0726485861
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## 2480 apr thu rain -0.0726485861
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## 2484 sep tue rain -0.0726485861
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## 2489 sep thu rain -0.0726485861
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## 2491 sep tue rain -0.0726485861
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## 2495 aug sat rain -0.0726485861
## 2496 sep fri rain -0.0726485861
## 2497 mar mon rain -0.0726485861
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## 2500 sep sun rain -0.0726485861
## 2501 sep mon rain -0.0726485861
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## 2504 aug sun rain -0.0726485861
## 2505 sep fri rain -0.0726485861
## 2506 mar mon rain -0.0726485861
## 2507 jul fri rain -0.0726485861
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## 2510 oct mon rain -0.0726485861
## 2511 aug sat rain -0.0726485861
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## 2547 dec sun rain -0.0726485861
## 2548 dec wed rain -0.0726485861
## 2549 dec thu rain -0.0726485861
## 2550 dec mon rain -0.0726485861
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## 2553 dec mon rain -0.0726485861
## 2554 dec fri rain -0.0726485861
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## 2558 jul sat rain -0.0726485861
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## 2564 sep sun rain -0.0726485861
## 2565 sep sun rain -0.0726485861
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## 3102 sep sun fire_yes_no 1.0000000000
## 3103 sep sun fire_yes_no 1.0000000000
## 3104 sep sun fire_yes_no 1.0000000000
## 3105 sep wed fire_yes_no 1.0000000000
## 3106 sep thu fire_yes_no 1.0000000000
## 3107 sep thu fire_yes_no 1.0000000000
## 3108 sep thu fire_yes_no 1.0000000000
## 3109 sep thu fire_yes_no 1.0000000000
## 3110 sep thu fire_yes_no 1.0000000000
## 3111 sep thu fire_yes_no 1.0000000000
## 3112 sep thu fire_yes_no 1.0000000000
## 3113 sep sat fire_yes_no 1.0000000000
## 3114 sep sat fire_yes_no 1.0000000000
## 3115 sep sat fire_yes_no 1.0000000000
## 3116 sep sat fire_yes_no 1.0000000000
## 3117 sep mon fire_yes_no 1.0000000000
## 3118 sep mon fire_yes_no 1.0000000000
## 3119 sep mon fire_yes_no 1.0000000000
## 3120 sep mon fire_yes_no 1.0000000000
## 3121 sep mon fire_yes_no 1.0000000000
## 3122 sep mon fire_yes_no 1.0000000000
## 3123 sep mon fire_yes_no 1.0000000000
## 3124 sep mon fire_yes_no 1.0000000000
## 3125 sep mon fire_yes_no 1.0000000000
## 3126 sep fri fire_yes_no 1.0000000000
## 3127 sep fri fire_yes_no 1.0000000000
## 3128 sep fri fire_yes_no 1.0000000000
## 3129 sep fri fire_yes_no 1.0000000000
## 3130 sep fri fire_yes_no 1.0000000000
## 3131 sep fri fire_yes_no 1.0000000000
## 3132 sep fri fire_yes_no 1.0000000000
## 3133 sep fri fire_yes_no 1.0000000000
## 3134 sep fri fire_yes_no 1.0000000000
## 3135 sep fri fire_yes_no 1.0000000000
## 3136 sep fri fire_yes_no 1.0000000000
## 3137 sep fri fire_yes_no 1.0000000000
## 3138 sep tue fire_yes_no 1.0000000000
## 3139 sep tue fire_yes_no 1.0000000000
## 3140 sep tue fire_yes_no 1.0000000000
## 3141 sep tue fire_yes_no 1.0000000000
## 3142 sep sat fire_yes_no 1.0000000000
## 3143 sep sun fire_yes_no 1.0000000000
## 3144 sep fri fire_yes_no 1.0000000000
## 3145 sep sat fire_yes_no 1.0000000000
## 3146 aug sat fire_yes_no 1.0000000000
## 3147 jul wed fire_yes_no 1.0000000000
## 3148 aug thu fire_yes_no 1.0000000000
## 3149 aug wed fire_yes_no 1.0000000000
## 3150 aug thu fire_yes_no 1.0000000000
## 3151 aug sat fire_yes_no 1.0000000000
## 3152 aug sun fire_yes_no 1.0000000000
## 3153 sep sun fire_yes_no 1.0000000000
## 3154 aug fri fire_yes_no 1.0000000000
## 3155 feb mon fire_yes_no 1.0000000000
## 3156 sep fri fire_yes_no 1.0000000000
## 3157 sep sun fire_yes_no 1.0000000000
## 3158 feb sun fire_yes_no 1.0000000000
## 3159 sep sun fire_yes_no 1.0000000000
## 3160 aug sun fire_yes_no 1.0000000000
## 3161 jun wed fire_yes_no 1.0000000000
## 3162 sep thu fire_yes_no 1.0000000000
## 3163 sep wed fire_yes_no 1.0000000000
## 3164 sep sat fire_yes_no 1.0000000000
## 3165 sep fri fire_yes_no 1.0000000000
## 3166 feb fri fire_yes_no 1.0000000000
## 3167 jul mon fire_yes_no 1.0000000000
## 3168 aug thu fire_yes_no 1.0000000000
## 3169 jul tue fire_yes_no 1.0000000000
## 3170 aug sun fire_yes_no 1.0000000000
## 3171 aug sun fire_yes_no 1.0000000000
## 3172 aug wed fire_yes_no 1.0000000000
## 3173 jul sun fire_yes_no 1.0000000000
## 3174 sep sat fire_yes_no 1.0000000000
## 3175 aug sat fire_yes_no 1.0000000000
## 3176 aug mon fire_yes_no 1.0000000000
## 3177 aug sun fire_yes_no 1.0000000000
## 3178 aug sat fire_yes_no 1.0000000000
## 3179 aug sun fire_yes_no 1.0000000000
## 3180 aug mon fire_yes_no 1.0000000000
## 3181 aug sat fire_yes_no 1.0000000000
## 3182 sep fri fire_yes_no 1.0000000000
## 3183 aug mon fire_yes_no 1.0000000000
## 3184 apr mon fire_yes_no 1.0000000000
## 3185 sep fri fire_yes_no 1.0000000000
## 3186 aug wed fire_yes_no 1.0000000000
## 3187 aug fri fire_yes_no 1.0000000000
## 3188 aug wed fire_yes_no 1.0000000000
## 3189 aug sat fire_yes_no 1.0000000000
## 3190 aug sat fire_yes_no 1.0000000000
## 3191 sep sun fire_yes_no 1.0000000000
## 3192 feb tue fire_yes_no 1.0000000000
## 3193 feb tue fire_yes_no 1.0000000000
## 3194 feb sat fire_yes_no 1.0000000000
## 3195 mar mon fire_yes_no 1.0000000000
## 3196 mar wed fire_yes_no 1.0000000000
## 3197 mar thu fire_yes_no 1.0000000000
## 3198 apr sun fire_yes_no 1.0000000000
## 3199 may fri fire_yes_no 1.0000000000
## 3200 jun mon fire_yes_no 1.0000000000
## 3201 jun sat fire_yes_no 1.0000000000
## 3202 jun thu fire_yes_no 1.0000000000
## 3203 jun thu fire_yes_no 1.0000000000
## 3204 jul thu fire_yes_no 1.0000000000
## 3205 jul sun fire_yes_no 1.0000000000
## 3206 jul sun fire_yes_no 1.0000000000
## 3207 jul mon fire_yes_no 1.0000000000
## 3208 jul thu fire_yes_no 1.0000000000
## 3209 aug sun fire_yes_no 1.0000000000
## 3210 aug sun fire_yes_no 1.0000000000
## 3211 aug mon fire_yes_no 1.0000000000
## 3212 aug tue fire_yes_no 1.0000000000
## 3213 aug tue fire_yes_no 1.0000000000
## 3214 aug tue fire_yes_no 1.0000000000
## 3215 aug fri fire_yes_no 1.0000000000
## 3216 aug sat fire_yes_no 1.0000000000
## 3217 aug mon fire_yes_no 1.0000000000
## 3218 aug tue fire_yes_no 1.0000000000
## 3219 aug tue fire_yes_no 1.0000000000
## 3220 aug tue fire_yes_no 1.0000000000
## 3221 aug wed fire_yes_no 1.0000000000
## 3222 aug wed fire_yes_no 1.0000000000
## 3223 aug thu fire_yes_no 1.0000000000
## 3224 aug fri fire_yes_no 1.0000000000
## 3225 aug fri fire_yes_no 1.0000000000
## 3226 aug sun fire_yes_no 1.0000000000
## 3227 aug sun fire_yes_no 1.0000000000
## 3228 aug sun fire_yes_no 1.0000000000
# Vis of freq per month
g1 <- ggplot(forestfiresmm,aes(x=forestfiresmm$month,y=forestfiresmm$fire_yes_no))
g1 + geom_bar(stat = "identity",aes(fill=factor(month)))+labs(title = "Significant fires per month")+xlab("Month")+ylab("Freq of Fires")+theme_classic()
# Vis of freq per day
g2 <- ggplot(forestfires,aes(forestfires$day))
g2 + geom_bar(aes(fill=factor(day)))+labs(title = "Freq of observations per day")+xlab("Day")+ylab("Count")+theme_classic()
#install.packages("wesanderson") # this package has nice colors for graphs
#library(wesanderson)
mid <- mean(forestfires.scaled$area) # store the average value for area
# melted data frame , using scaled values
## Any variables seem to vary by month? or correlated?
ggplot(data = mdata, aes(x = month, y = value, fill = variable)) +
# `geom_col()` uses `stat_identity()`: it leaves the data as is.
geom_col(position = 'dodge')
# Dot plot, freq of area burned by month
# Basic scatter plot
g1 = ggplot(data = forestfiresmm.scaled, aes_string(x = "month", y = "area")) +
geom_point()
# Change the point size, and shape
g1 = g1 + geom_point(size = 1, shape = 23)
g1
# two variables continuous , plot for correlation
avg <- mean(forestfires.scaled$area)
c <- ggplot(forestfires.scaled, aes(ISI, area))
# Default plot
c + geom_bin2d()
# Change the number of bins
c + geom_bin2d(bins = 15)+geom_hline(aes(yintercept = avg))
###### DECISION TREE ######
# Set up
# Plot Observations
## We see the construction of the forest and points
plot(forestfires_na_factor$X,forestfires$Y)
## View fire data
summary(forestfires_na_factor$area)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 0.52 12.85 6.57 1090.84
# Note the mean and median
# View points where above average forest fire destruction
points(forestfires_na_factor$X[forestfires_na_factor$area>=.52], forestfires_na_factor$Y[forestfires_na_factor$area>=.52], col="green", pch=20)
# Check the RH over areas
## View area
summary(forestfires_na_factor$area)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 0.52 12.85 6.57 1090.84
points(forestfires_na_factor$X[forestfires_na_factor$area>=.52], forestfires_na_factor$Y[forestfires_na_factor$area>=.52], col="red", pch=20)
# View wether the data is linear
plot(forestfires_na_factor$X,forestfires_na_factor$area)
plot(forestfires_na_factor$Y,forestfires_na_factor$area)
# Linear Regression Model
latlonlm = lm(area ~ X + Y, data = forestfires_na_factor)
summary(latlonlm)
##
## Call:
## lm(formula = area ~ X + Y, data = forestfires_na_factor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.09 -13.86 -10.08 -5.37 1075.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3976 10.1970 0.235 0.814
## X 1.5203 1.4383 1.057 0.291
## Y 0.7793 2.7058 0.288 0.773
##
## Residual standard error: 63.65 on 514 degrees of freedom
## Multiple R-squared: 0.004178, Adjusted R-squared: 0.0003036
## F-statistic: 1.078 on 2 and 514 DF, p-value: 0.3409
# R-Squared is around .3409 or 34%
# The linear model plots a blue money sign every time it thinks RH is above median value.
# CART model
latlontree = rpart(area ~ X + Y, data=forestfires_na_factor)# Plot the tree using prp command defined in rpart.plot package
prp(latlontree)
fittedvalues = predict(latlontree)
# Simplifying Tree by increasing minBucket
latlontree = rpart(area ~ X + Y, data=forestfires_na_factor, minbucket=50)
# plot(latlontree)
# text(latlontree)
# Prediction with Regression Trees
set.seed(123)
#split=sample.split(forestfires_na_factor$area, SplitRatio = 0.7)
split=sample.split(forestfires_na_factor$area, SplitRatio = 0.7)
train=subset(forestfires_na_factor, split==TRUE)
test=subset(forestfires_na_factor, split==FALSE)
# CV
CVdata <- dplyr::select(forestfires_na_factor,c(-1,-2))
Split_M <- as.matrix(CVdata)
Papers_M_N1 <- apply(Split_M, 1, function(i) round(i/sum(i),3))
Papers_Matrix_Norm <- t(Papers_M_N1)
# Create a CART model
tree = rpart(area ~ X + Y + FFMC + DMC + ISI + temp + RH + wind + rain, data=train)
prp(tree)
# Regression Tree Predictions
tree.pred = predict(tree, newdata=test)
tree.sse = sum((tree.pred - test$area)^2)
tree.sse
## [1] 83860.14
# Visualize regression output
plot(forestfires_na_factor$X, forestfires_na_factor$Y)
points(forestfires_na_factor$X[forestfires_na_factor$area>=.52], forestfires_na_factor$Y[forestfires_na_factor$area>=.52], col="red", pch=20)> latlonlm$fitted.values
## logical(0)
points(forestfires_na_factor$X[latlonlm$fitted.values >= .52], forestfires_na_factor$Y[latlonlm$fitted.values >= .52], col="blue", pch="$")
# Create basic x and y cord plot in ggplot
pointgg = ggplot(forestfires_na_factor,aes(x = forestfires_na_factor$X, y = forestfires_na_factor$Y))
pointgg = pointgg + geom_point(color="red")
pointgg = pointgg + scale_y_reverse(breaks = pretty(forestfires_na_factor$Y,n=9)) + scale_x_continuous(position = 'top',breaks = pretty(forestfires_na_factor$X, n = 9))
pointgg = pointgg + labs(title = "Montesinho Natural Park fires",x="",y="")
pointgg
# SVM and Random Forest
ForestFiresWith <- read_excel("ForestFiresWith.xlsx")
ff <- ForestFiresWith
View(ff)
#corrplot(ff, method = "number")
corrplot(corrgram(ff))
summary(ff)
## X Y month day
## Min. :1.000 Min. :2.0 Length:517 Length:517
## 1st Qu.:3.000 1st Qu.:4.0 Class :character Class :character
## Median :4.000 Median :4.0 Mode :character Mode :character
## Mean :4.669 Mean :4.3
## 3rd Qu.:7.000 3rd Qu.:5.0
## Max. :9.000 Max. :9.0
## FFMC DMC DC ISI
## Min. :18.70 Min. : 1.1 Min. : 7.9 Min. : 0.000
## 1st Qu.:90.20 1st Qu.: 68.6 1st Qu.:437.7 1st Qu.: 6.500
## Median :91.60 Median :108.3 Median :664.2 Median : 8.400
## Mean :90.64 Mean :110.9 Mean :547.9 Mean : 9.022
## 3rd Qu.:92.90 3rd Qu.:142.4 3rd Qu.:713.9 3rd Qu.:10.800
## Max. :96.20 Max. :291.3 Max. :860.6 Max. :56.100
## temperature relative humidity wind speeds rain amount
## Min. : 2.20 Min. : 15.00 Min. :0.400 Min. :0.00000
## 1st Qu.:15.50 1st Qu.: 33.00 1st Qu.:2.700 1st Qu.:0.00000
## Median :19.30 Median : 42.00 Median :4.000 Median :0.00000
## Mean :18.89 Mean : 44.29 Mean :4.018 Mean :0.02166
## 3rd Qu.:22.80 3rd Qu.: 53.00 3rd Qu.:4.900 3rd Qu.:0.00000
## Max. :33.30 Max. :100.00 Max. :9.400 Max. :6.40000
## area fire__no_yes
## Min. : 0.00 Min. :0.0000
## 1st Qu.: 0.00 1st Qu.:0.0000
## Median : 0.52 Median :1.0000
## Mean : 12.85 Mean :0.5222
## 3rd Qu.: 6.57 3rd Qu.:1.0000
## Max. :1090.84 Max. :1.0000
ff <- ff[,-13]
str(ff)
## Classes 'tbl_df', 'tbl' and 'data.frame': 517 obs. of 13 variables:
## $ X : num 7 2 2 3 5 6 6 3 2 6 ...
## $ Y : num 5 4 2 4 4 5 4 4 4 3 ...
## $ month : chr "apr" "jan" "feb" "mar" ...
## $ day : chr "sun" "sat" "sat" "sat" ...
## $ FFMC : num 81.9 82.1 79.5 69 85.2 75.1 75.1 86.9 93.4 91 ...
## $ DMC : num 3 3.7 3.6 2.4 4.9 4.4 4.4 6.6 15 14.6 ...
## $ DC : num 7.9 9.3 15.3 15.5 15.8 16.2 16.2 18.7 25.6 25.6 ...
## $ ISI : num 3.5 2.9 1.8 0.7 6.3 1.9 1.9 3.2 11.4 12.3 ...
## $ temperature : num 13.4 5.3 4.6 17.4 7.5 4.6 5.1 8.8 15.2 13.7 ...
## $ relative humidity: num 75 78 59 24 46 82 77 35 19 33 ...
## $ wind speeds : num 1.8 3.1 0.9 5.4 8 6.3 5.4 3.1 7.6 9.4 ...
## $ rain amount : num 0 0 0 0 0 0 0 0 0 0 ...
## $ fire__no_yes : num 0 0 1 0 1 1 1 1 0 1 ...
sapply(ff, sd)
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm
## = na.rm): NAs introduced by coercion
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm
## = na.rm): NAs introduced by coercion
## X Y month day
## 2.3137778 1.2299004 NA NA
## FFMC DMC DC ISI
## 5.5201108 64.0464822 248.0661917 4.5594772
## temperature relative humidity wind speeds rain amount
## 5.8066253 16.3174692 1.7916526 0.2959591
## fire__no_yes
## 0.4999888
trainRatio <- .67
set.seed(1016) # Set Seed so that same sample can be reproduced in future also
sample <- sample.int(n = nrow(ff), size = floor(trainRatio*nrow(ff)), replace = FALSE)
ff$X <- log(ff$X)
testdata <- ff[-sample, ]
testdata
## # A tibble: 171 x 13
## X Y month day FFMC DMC DC ISI temperature
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.95 5 apr sun 81.9 3 7.9 3.5 13.4
## 2 1.79 5 feb tue 75.1 4.4 16.2 1.9 4.6
## 3 1.10 4 feb wed 86.9 6.6 18.7 3.2 8.8
## 4 1.79 3 apr sun 91 14.6 25.6 12.3 13.7
## 5 1.10 4 feb sat 83.9 8 30.2 2.6 12.7
## 6 1.61 5 mar thu 90.9 18.9 30.6 8 11.6
## 7 1.79 5 mar mon 87.2 15.1 36.9 7.1 10.2
## 8 0.693 2 feb fri 86.6 13.2 43 5.3 12.3
## 9 1.79 5 mar thu 91.3 20.6 43.5 8.5 13.3
## 10 1.39 5 feb sun 85 9 56.9 3.5 10.1
## # … with 161 more rows, and 4 more variables: `relative humidity` <dbl>,
## # `wind speeds` <dbl>, `rain amount` <dbl>, fire__no_yes <dbl>
testdata <- testdata[, -c(3:4)]
summary(testdata)
## X Y FFMC DMC
## Min. :0.0000 Min. :2.00 Min. :50.40 Min. : 3.00
## 1st Qu.:0.6931 1st Qu.:4.00 1st Qu.:90.10 1st Qu.: 51.75
## Median :1.3863 Median :4.00 Median :91.60 Median : 97.90
## Mean :1.3224 Mean :4.17 Mean :90.48 Mean :100.55
## 3rd Qu.:1.9459 3rd Qu.:5.00 3rd Qu.:92.50 3rd Qu.:130.90
## Max. :2.1972 Max. :9.00 Max. :96.10 Max. :276.30
## DC ISI temperature relative humidity
## Min. : 7.9 Min. : 0.400 Min. : 4.60 Min. :17.00
## 1st Qu.:399.9 1st Qu.: 6.700 1st Qu.:14.65 1st Qu.:32.50
## Median :664.5 Median : 8.400 Median :18.70 Median :41.00
## Mean :536.5 Mean : 8.763 Mean :18.18 Mean :44.82
## 3rd Qu.:713.5 3rd Qu.:10.100 3rd Qu.:21.85 3rd Qu.:54.00
## Max. :825.1 Max. :22.600 Max. :30.60 Max. :99.00
## wind speeds rain amount fire__no_yes
## Min. :0.900 Min. :0.00000 Min. :0.0000
## 1st Qu.:2.700 1st Qu.:0.00000 1st Qu.:0.0000
## Median :4.000 Median :0.00000 Median :1.0000
## Mean :4.029 Mean :0.01287 Mean :0.5322
## 3rd Qu.:5.400 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :9.400 Max. :1.40000 Max. :1.0000
traindata <- ff[sample, ]
traindata <- traindata[, -c(3:4)]
summary(traindata)
## X Y FFMC DMC
## Min. :0.000 Min. :2.000 Min. :18.70 Min. : 1.10
## 1st Qu.:1.099 1st Qu.:4.000 1st Qu.:90.30 1st Qu.: 80.75
## Median :1.386 Median :4.000 Median :91.70 Median :111.70
## Mean :1.403 Mean :4.364 Mean :90.73 Mean :115.97
## 3rd Qu.:1.946 3rd Qu.:5.000 3rd Qu.:93.10 3rd Qu.:146.97
## Max. :2.197 Max. :9.000 Max. :96.20 Max. :291.30
## DC ISI temperature relative humidity
## Min. : 9.3 Min. : 0.00 Min. : 2.20 Min. : 15.00
## 1st Qu.:474.9 1st Qu.: 6.30 1st Qu.:16.10 1st Qu.: 33.00
## Median :661.8 Median : 8.40 Median :19.60 Median : 42.00
## Mean :553.6 Mean : 9.15 Mean :19.24 Mean : 44.03
## 3rd Qu.:713.9 3rd Qu.:11.30 3rd Qu.:23.30 3rd Qu.: 53.00
## Max. :860.6 Max. :56.10 Max. :33.30 Max. :100.00
## wind speeds rain amount fire__no_yes
## Min. :0.400 Min. :0.00000 Min. :0.0000
## 1st Qu.:2.700 1st Qu.:0.00000 1st Qu.:0.0000
## Median :4.000 Median :0.00000 Median :1.0000
## Mean :4.012 Mean :0.02601 Mean :0.5173
## 3rd Qu.:4.900 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :9.400 Max. :6.40000 Max. :1.0000
probit2 <- glm(traindata$fire__no_yes ~., family = binomial(link = "probit"), data=traindata[,-(length(traindata))])
logit2 <- glm(traindata$fire__no_yes ~., family = "binomial", data=traindata[,-(length(traindata))])
summary(probit2)
##
## Call:
## glm(formula = traindata$fire__no_yes ~ ., family = binomial(link = "probit"),
## data = traindata[, -(length(traindata))])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5800 -1.2101 0.9448 1.0934 1.7574
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.404e+00 1.951e+00 -1.232 0.218
## X -2.242e-02 1.330e-01 -0.169 0.866
## Y 7.824e-02 6.516e-02 1.201 0.230
## FFMC 2.134e-02 2.131e-02 1.002 0.317
## DMC -4.241e-05 1.594e-03 -0.027 0.979
## DC 5.414e-04 4.190e-04 1.292 0.196
## ISI -7.071e-03 1.828e-02 -0.387 0.699
## temperature -2.772e-04 1.850e-02 -0.015 0.988
## `relative humidity` -4.395e-03 5.911e-03 -0.743 0.457
## `wind speeds` 4.126e-02 4.156e-02 0.993 0.321
## `rain amount` 1.005e-01 2.206e-01 0.456 0.649
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 479.24 on 345 degrees of freedom
## Residual deviance: 468.43 on 335 degrees of freedom
## AIC: 490.43
##
## Number of Fisher Scoring iterations: 5
summary(logit2)
##
## Call:
## glm(formula = traindata$fire__no_yes ~ ., family = "binomial",
## data = traindata[, -(length(traindata))])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5808 -1.2077 0.9434 1.0929 1.7544
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.887e+00 3.318e+00 -1.172 0.241
## X -3.643e-02 2.133e-01 -0.171 0.864
## Y 1.278e-01 1.049e-01 1.218 0.223
## FFMC 3.429e-02 3.643e-02 0.941 0.347
## DMC -9.527e-05 2.557e-03 -0.037 0.970
## DC 8.820e-04 6.740e-04 1.309 0.191
## ISI -1.095e-02 2.985e-02 -0.367 0.714
## temperature -2.139e-04 2.972e-02 -0.007 0.994
## `relative humidity` -7.108e-03 9.503e-03 -0.748 0.455
## `wind speeds` 6.738e-02 6.689e-02 1.007 0.314
## `rain amount` 1.503e-01 3.715e-01 0.405 0.686
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 479.24 on 345 degrees of freedom
## Residual deviance: 468.44 on 335 degrees of freedom
## AIC: 490.44
##
## Number of Fisher Scoring iterations: 4
predictedlogit <- plogis(predict(logit2, testdata))
predictedprobit <- plogis(predict(probit2, testdata))
table(predictedlogit > 0.5, testdata$fire__no_yes)
##
## 0 1
## FALSE 35 29
## TRUE 45 62
### SVM Model
traindata2 <- traindata
svmclassifier = svm(formula = traindata2$`fire__no_yes` ~ .,
data = traindata2,
type = 'C-classification',
kernel = 'linear')
testdata2 <- testdata
#y_pred <- predict(svmclassifier, newdata = testdata2[-9])
y_pred <- predict(svmclassifier, newdata = testdata2)
cm <- table(testdata2$`fire__no_yes`, y_pred)
cm
## y_pred
## 0 1
## 0 22 58
## 1 19 72
prediction <- predict(svmclassifier, newdata = testdata2)
results <- data.frame(testdata2$`fire__no_yes`, prediction)
colnames(results) <- c("Actual", "Prediction")
str(results)
## 'data.frame': 171 obs. of 2 variables:
## $ Actual : num 0 1 1 1 0 0 1 0 1 1 ...
## $ Prediction: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
results$Prediction <- as.factor(results$Prediction)
results$Actual <- as.factor(results$Actual)
confusionMatrix(results$Prediction, results$Actual)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 22 19
## 1 58 72
##
## Accuracy : 0.5497
## 95% CI : (0.4719, 0.6258)
## No Information Rate : 0.5322
## P-Value [Acc > NIR] : 0.3514
##
## Kappa : 0.0682
##
## Mcnemar's Test P-Value : 1.488e-05
##
## Sensitivity : 0.2750
## Specificity : 0.7912
## Pos Pred Value : 0.5366
## Neg Pred Value : 0.5538
## Prevalence : 0.4678
## Detection Rate : 0.1287
## Detection Prevalence : 0.2398
## Balanced Accuracy : 0.5331
##
## 'Positive' Class : 0
##
svmclassifier2 = svm(formula = traindata2$`fire__no_yes` ~ .,
data = traindata2,
type = 'C-classification',
kernel = 'polynomial')
y_pred <- predict(svmclassifier2, newdata = testdata2)
cm <- table(testdata2$`fire__no_yes`, y_pred)
cm
## y_pred
## 0 1
## 0 25 55
## 1 16 75
prediction <- predict(svmclassifier2, newdata = testdata2)
results <- data.frame(testdata2$`fire__no_yes`, prediction)
colnames(results) <- c("Actual", "Prediction")
str(results)
## 'data.frame': 171 obs. of 2 variables:
## $ Actual : num 0 1 1 1 0 0 1 0 1 1 ...
## $ Prediction: Factor w/ 2 levels "0","1": 1 2 1 2 1 1 2 2 1 1 ...
results$Prediction <- as.factor(results$Prediction)
results$Actual <- as.factor(results$Actual)
confusionMatrix(results$Prediction, results$Actual)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 25 16
## 1 55 75
##
## Accuracy : 0.5848
## 95% CI : (0.5071, 0.6595)
## No Information Rate : 0.5322
## P-Value [Acc > NIR] : 0.09606
##
## Kappa : 0.1408
##
## Mcnemar's Test P-Value : 6.49e-06
##
## Sensitivity : 0.3125
## Specificity : 0.8242
## Pos Pred Value : 0.6098
## Neg Pred Value : 0.5769
## Prevalence : 0.4678
## Detection Rate : 0.1462
## Detection Prevalence : 0.2398
## Balanced Accuracy : 0.5683
##
## 'Positive' Class : 0
##
svmclassifier3 = svm(formula = traindata2$`fire__no_yes` ~ .,
data = traindata2,
type = 'C-classification',
kernel = 'sigmoid')
y_pred <- predict(svmclassifier3, newdata = testdata2)
cm <- table(testdata2$`fire__no_yes`, y_pred)
cm
## y_pred
## 0 1
## 0 27 53
## 1 33 58
prediction <- predict(svmclassifier3, newdata = testdata2)
results <- data.frame(testdata2$`fire__no_yes`, prediction)
colnames(results) <- c("Actual", "Prediction")
str(results)
## 'data.frame': 171 obs. of 2 variables:
## $ Actual : num 0 1 1 1 0 0 1 0 1 1 ...
## $ Prediction: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
results$Prediction <- as.factor(results$Prediction)
results$Actual <- as.factor(results$Actual)
confusionMatrix(results$Prediction, results$Actual)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 27 33
## 1 53 58
##
## Accuracy : 0.4971
## 95% CI : (0.4198, 0.5744)
## No Information Rate : 0.5322
## P-Value [Acc > NIR] : 0.84043
##
## Kappa : -0.0255
##
## Mcnemar's Test P-Value : 0.04048
##
## Sensitivity : 0.3375
## Specificity : 0.6374
## Pos Pred Value : 0.4500
## Neg Pred Value : 0.5225
## Prevalence : 0.4678
## Detection Rate : 0.1579
## Detection Prevalence : 0.3509
## Balanced Accuracy : 0.4874
##
## 'Positive' Class : 0
##
svmclassifier4 = svm(formula = traindata2$`fire__no_yes` ~ .,
data = traindata2,
type = 'C-classification',
kernel = 'radial')
y_pred <- predict(svmclassifier4, newdata = testdata2)
cm <- table(testdata2$`fire__no_yes`, y_pred)
cm
## y_pred
## 0 1
## 0 44 36
## 1 41 50
prediction <- predict(svmclassifier4, newdata = testdata2)
results <- data.frame(testdata2$`fire__no_yes`, prediction)
colnames(results) <- c("Actual", "Prediction")
str(results)
## 'data.frame': 171 obs. of 2 variables:
## $ Actual : num 0 1 1 1 0 0 1 0 1 1 ...
## $ Prediction: Factor w/ 2 levels "0","1": 1 2 1 2 1 1 1 1 1 1 ...
results$Prediction <- as.factor(results$Prediction)
results$Actual <- as.factor(results$Actual)
confusionMatrix(results$Prediction, results$Actual)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 44 41
## 1 36 50
##
## Accuracy : 0.5497
## 95% CI : (0.4719, 0.6258)
## No Information Rate : 0.5322
## P-Value [Acc > NIR] : 0.3514
##
## Kappa : 0.0991
##
## Mcnemar's Test P-Value : 0.6485
##
## Sensitivity : 0.5500
## Specificity : 0.5495
## Pos Pred Value : 0.5176
## Neg Pred Value : 0.5814
## Prevalence : 0.4678
## Detection Rate : 0.2573
## Detection Prevalence : 0.4971
## Balanced Accuracy : 0.5497
##
## 'Positive' Class : 0
##
###created XY coordinates and adding it to ff
df <- paste(ff$X,",",ff$Y)
df <- as.data.frame(df)
colnames(df) <- "coordinates"
df
## coordinates
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## 473 0.693147180559945 , 5
## 474 2.07944154167984 , 6
## 475 2.07944154167984 , 6
## 476 1.38629436111989 , 3
## 477 1.79175946922805 , 5
## 478 1.79175946922805 , 5
## 479 1.79175946922805 , 5
## 480 1.38629436111989 , 4
## 481 1.79175946922805 , 5
## 482 1.6094379124341 , 4
## 483 1.79175946922805 , 3
## 484 0 , 4
## 485 1.79175946922805 , 5
## 486 1.79175946922805 , 3
## 487 1.79175946922805 , 3
## 488 0 , 4
## 489 1.79175946922805 , 5
## 490 1.38629436111989 , 3
## 491 1.94591014905531 , 4
## 492 1.38629436111989 , 4
## 493 2.07944154167984 , 6
## 494 1.38629436111989 , 5
## 495 1.79175946922805 , 3
## 496 2.07944154167984 , 6
## 497 1.79175946922805 , 3
## 498 0.693147180559945 , 2
## 499 0 , 4
## 500 0 , 4
## 501 1.38629436111989 , 5
## 502 1.38629436111989 , 5
## 503 1.09861228866811 , 4
## 504 2.07944154167984 , 4
## 505 1.94591014905531 , 4
## 506 1.09861228866811 , 5
## 507 1.38629436111989 , 5
## 508 1.94591014905531 , 4
## 509 0 , 3
## 510 0 , 4
## 511 1.94591014905531 , 4
## 512 1.94591014905531 , 4
## 513 1.79175946922805 , 5
## 514 1.38629436111989 , 3
## 515 1.6094379124341 , 4
## 516 0.693147180559945 , 5
## 517 1.79175946922805 , 5
ff2 <- cbind(ForestFiresWith ,df)
ff2 <- as.data.frame(ff2)
## no need to X and Y columns when we have x,y column
ff2 <- ff2[,c(5:15)]
str(ff2)
## 'data.frame': 517 obs. of 11 variables:
## $ FFMC : num 81.9 82.1 79.5 69 85.2 75.1 75.1 86.9 93.4 91 ...
## $ DMC : num 3 3.7 3.6 2.4 4.9 4.4 4.4 6.6 15 14.6 ...
## $ DC : num 7.9 9.3 15.3 15.5 15.8 16.2 16.2 18.7 25.6 25.6 ...
## $ ISI : num 3.5 2.9 1.8 0.7 6.3 1.9 1.9 3.2 11.4 12.3 ...
## $ temperature : num 13.4 5.3 4.6 17.4 7.5 4.6 5.1 8.8 15.2 13.7 ...
## $ relative humidity: num 75 78 59 24 46 82 77 35 19 33 ...
## $ wind speeds : num 1.8 3.1 0.9 5.4 8 6.3 5.4 3.1 7.6 9.4 ...
## $ rain amount : num 0 0 0 0 0 0 0 0 0 0 ...
## $ area : num 0 0 6.84 0 24.24 ...
## $ fire__no_yes : num 0 0 1 0 1 1 1 1 0 1 ...
## $ coordinates : Factor w/ 36 levels "0 , 2","0 , 3",..: 26 7 5 10 17 22 21 10 7 20 ...
View(ff2)
## Using tapply to sum up the total area burn per coordinate X,Y
areaburnedbycoord <- tapply(ff2$area, ff2$coordinates, FUN = sum)
areaburnedbycoord <- cbind(coordinates = rownames(areaburnedbycoord), areaburnedbycoord)
colnames(areaburnedbycoord) <- c("coordinates", "total_area_burned")
areaburnedbycoord <- as.data.frame(areaburnedbycoord)
summary(areaburnedbycoord)
## coordinates total_area_burned
## 0 , 2 : 1 0 : 3
## 0 , 3 : 1 115.47 : 1
## 0 , 4 : 1 12.18 : 1
## 0 , 5 : 1 126.35 : 1
## 0.693147180559945 , 2: 1 1265.3 : 1
## 0.693147180559945 , 3: 1 1384.05: 1
## (Other) :30 (Other):28
barburn <- ggplot(data = areaburnedbycoord, aes(x= areaburnedbycoord$coordinates, y = areaburnedbycoord$total_area_burned))
barburn <- barburn + geom_bar(stat = "identity", width = .5, color = "black", size =1)
barburn <- barburn + ggtitle("Total Area Burnt") + labs(y="Hectares Burnt", x = "Coordinate plane")
barburn
##This code did not work
## Using tapply to sum up the total times there was a fire per coordinate X,Y
##ff2 <- ff2 %>% filter(ff2$fire__no_yes != 0)
##str(ff2)
##freqofburnedarea <- tapply(as.numeric(ff2$fire__no_yes), ff2$coordinates, FUN = length)
##freqofburnedarea <- cbind(coordinates = rownames(freqofburnedarea), freqofburnedarea)
##colnames(freqofburnedarea) <- cbind("coordinates", "freq_of_fires")
freqofburnedarea <- ff2[1:36,1:2]
colnames(freqofburnedarea) <- cbind("coordinates", "freq_of_fires")
summary(freqofburnedarea)
## coordinates freq_of_fires
## Min. :69.00 Min. : 2.400
## 1st Qu.:83.90 1st Qu.: 6.175
## Median :85.10 Median : 9.200
## Mean :85.44 Mean :11.358
## 3rd Qu.:88.55 3rd Qu.:17.225
## Max. :93.40 Max. :24.900
sum(freqofburnedarea$freq_of_fires)
## [1] 408.9
sum(ff2$fire__no_yes)
## [1] 270
barfreq <- ggplot(data = freqofburnedarea, aes(x= freqofburnedarea$coordinates, y = freqofburnedarea$freq_of_fires))
barfreq <- barfreq + geom_bar(stat = "identity", width = .5, color = "black", size =1)
barfreq <- barfreq + ggtitle("Number of Fires") + labs(y="Frequency", x = "Coordinate plane")
barfreq
## Warning: position_stack requires non-overlapping x intervals
# Decistion Tree
ff3 <- ff2[,-c(9,11)]
View(ff3)
ff3
## FFMC DMC DC ISI temperature relative humidity wind speeds
## 1 81.9 3.0 7.9 3.5 13.4 75 1.8
## 2 82.1 3.7 9.3 2.9 5.3 78 3.1
## 3 79.5 3.6 15.3 1.8 4.6 59 0.9
## 4 69.0 2.4 15.5 0.7 17.4 24 5.4
## 5 85.2 4.9 15.8 6.3 7.5 46 8.0
## 6 75.1 4.4 16.2 1.9 4.6 82 6.3
## 7 75.1 4.4 16.2 1.9 5.1 77 5.4
## 8 86.9 6.6 18.7 3.2 8.8 35 3.1
## 9 93.4 15.0 25.6 11.4 15.2 19 7.6
## 10 91.0 14.6 25.6 12.3 13.7 33 9.4
## 11 91.0 14.6 25.6 12.3 17.6 27 5.8
## 12 84.2 6.8 26.6 7.7 6.7 79 3.1
## 13 93.4 17.3 28.3 9.9 13.8 24 5.8
## 14 93.4 17.3 28.3 9.9 8.9 35 8.0
## 15 83.9 8.0 30.2 2.6 12.7 48 1.8
## 16 90.9 18.9 30.6 8.0 8.7 51 5.8
## 17 90.9 18.9 30.6 8.0 11.6 48 5.4
## 18 83.9 8.7 32.1 2.1 8.8 68 2.2
## 19 84.0 9.3 34.0 2.1 13.9 40 5.4
## 20 87.2 15.1 36.9 7.1 10.2 45 5.8
## 21 90.2 18.5 41.1 7.3 11.2 41 5.4
## 22 87.9 24.9 41.6 3.7 10.9 64 3.1
## 23 86.6 13.2 43.0 5.3 12.3 51 0.9
## 24 86.6 13.2 43.0 5.3 15.7 43 3.1
## 25 88.0 17.2 43.5 3.8 15.2 51 2.7
## 26 91.3 20.6 43.5 8.5 13.3 27 3.6
## 27 84.6 3.2 43.6 3.3 8.2 53 9.4
## 28 84.1 4.6 46.7 2.2 5.3 68 1.8
## 29 86.8 15.6 48.3 3.9 12.4 53 2.2
## 30 84.1 7.3 52.8 2.7 14.7 42 2.7
## 31 84.9 18.2 55.0 3.0 5.3 70 4.5
## 32 84.7 8.2 55.0 2.9 14.2 46 4.0
## 33 81.5 9.1 55.2 2.7 5.8 54 5.8
## 34 81.5 9.1 55.2 2.7 5.8 54 5.8
## 35 85.0 9.0 56.9 3.5 10.1 62 1.8
## 36 85.9 19.5 57.3 2.8 12.7 52 6.3
## 37 85.9 19.5 57.3 2.8 13.7 43 5.8
## 38 84.7 9.5 58.3 4.1 7.5 71 6.3
## 39 87.2 23.9 64.7 4.1 11.8 35 1.8
## 40 87.2 23.9 64.7 4.1 14.0 39 3.1
## 41 88.1 25.7 67.6 3.8 14.1 43 2.7
## 42 88.1 25.7 67.6 3.8 15.8 27 7.6
## 43 88.1 25.7 67.6 3.8 15.5 27 6.3
## 44 88.1 25.7 67.6 3.8 14.9 38 2.7
## 45 89.2 27.9 70.8 6.3 15.9 35 4.0
## 46 89.6 25.4 73.7 5.7 18.0 40 4.0
## 47 91.4 30.7 74.3 7.5 18.2 29 3.1
## 48 91.7 33.3 77.5 9.0 17.2 26 4.5
## 49 91.7 33.3 77.5 9.0 15.6 25 6.3
## 50 91.7 33.3 77.5 9.0 18.8 18 4.5
## 51 91.7 33.3 77.5 9.0 8.3 97 4.0
## 52 91.7 35.8 80.8 7.8 15.1 27 5.4
## 53 91.7 35.8 80.8 7.8 17.4 25 4.9
## 54 91.7 35.8 80.8 7.8 17.4 24 5.4
## 55 91.7 35.8 80.8 7.8 11.6 30 6.3
## 56 91.7 35.8 80.8 7.8 15.2 27 4.9
## 57 91.7 35.8 80.8 7.8 17.0 27 4.9
## 58 91.7 35.8 80.8 7.8 17.0 27 4.9
## 59 90.1 37.6 83.7 7.2 12.4 54 3.6
## 60 83.0 23.3 85.3 2.3 16.7 20 3.1
## 61 90.1 39.7 86.6 6.2 10.6 30 4.0
## 62 90.1 39.7 86.6 6.2 13.2 40 5.4
## 63 90.1 39.7 86.6 6.2 16.1 29 3.1
## 64 90.1 39.7 86.6 6.2 15.2 27 3.1
## 65 68.2 21.5 87.2 0.8 15.4 40 2.7
## 66 90.8 41.9 89.4 7.9 13.3 42 0.9
## 67 90.7 44.0 92.4 5.5 11.5 60 4.0
## 68 86.2 26.2 94.3 5.1 8.2 51 6.7
## 69 86.3 27.4 97.1 5.1 9.3 44 4.5
## 70 91.2 48.3 97.8 12.5 15.8 27 7.6
## 71 91.2 48.3 97.8 12.5 14.6 26 9.4
## 72 91.2 48.3 97.8 12.5 11.7 33 4.0
## 73 90.6 50.1 100.4 7.8 15.2 31 8.5
## 74 90.6 50.1 100.4 7.8 15.1 64 4.0
## 75 94.0 47.9 100.7 10.7 17.3 80 4.5
## 76 89.3 51.3 102.2 9.6 11.4 99 1.8
## 77 89.3 51.3 102.2 9.6 11.5 39 5.8
## 78 89.3 51.3 102.2 9.6 5.5 59 6.3
## 79 89.3 51.3 102.2 9.6 10.6 46 4.9
## 80 89.3 51.3 102.2 9.6 11.5 39 5.8
## 81 87.6 52.2 103.8 5.0 11.0 46 5.8
## 82 87.6 52.2 103.8 5.0 8.3 72 3.1
## 83 87.6 52.2 103.8 5.0 9.0 49 2.2
## 84 87.6 52.2 103.8 5.0 11.0 46 5.8
## 85 87.6 52.2 103.8 5.0 11.0 46 5.8
## 86 79.5 3.0 106.7 1.1 11.8 31 4.5
## 87 85.1 28.0 113.8 3.5 11.3 94 4.9
## 88 18.7 1.1 171.4 0.0 5.2 100 0.9
## 89 94.3 96.3 200.0 56.1 21.0 44 4.5
## 90 88.2 96.2 229.0 4.7 14.3 79 4.0
## 91 91.1 94.1 232.1 7.1 19.2 38 4.5
## 92 91.1 94.1 232.1 7.1 19.2 38 4.5
## 93 53.4 71.0 233.8 0.4 10.6 90 2.7
## 94 90.5 61.1 252.6 9.4 24.5 50 3.1
## 95 90.4 89.5 290.8 6.4 14.3 46 1.8
## 96 90.4 89.5 290.8 6.4 15.4 45 2.2
## 97 90.0 51.3 296.3 8.7 16.6 53 5.4
## 98 93.3 49.5 297.7 14.0 28.0 34 4.5
## 99 93.3 49.5 297.7 14.0 28.0 34 4.5
## 100 90.4 93.3 298.1 7.5 20.7 25 4.9
## 101 90.4 93.3 298.1 7.5 19.1 39 5.4
## 102 88.3 150.3 309.9 6.8 13.4 79 3.6
## 103 85.8 48.3 313.4 3.9 18.0 42 2.7
## 104 93.0 103.8 316.7 10.8 26.4 35 2.7
## 105 85.4 25.4 349.7 2.6 4.6 21 8.5
## 106 85.4 25.4 349.7 2.6 4.6 21 8.5
## 107 85.4 25.4 349.7 2.6 4.6 21 8.5
## 108 85.4 25.4 349.7 2.6 4.6 21 8.5
## 109 85.4 25.4 349.7 2.6 5.1 24 8.5
## 110 93.7 121.7 350.2 18.0 22.7 40 9.4
## 111 84.6 26.4 352.0 2.0 5.1 61 4.9
## 112 84.7 26.7 352.6 4.1 2.2 59 4.9
## 113 84.4 27.2 353.5 6.8 4.8 57 8.5
## 114 84.9 27.5 353.5 3.4 4.2 51 4.0
## 115 84.0 27.8 354.6 5.3 5.1 61 8.0
## 116 90.1 68.6 355.2 7.2 24.8 29 2.2
## 117 79.5 60.6 366.7 1.5 23.3 37 3.1
## 118 90.7 80.9 368.3 16.8 14.8 78 8.0
## 119 90.8 84.7 376.6 5.6 23.8 51 1.8
## 120 91.2 147.8 377.2 12.7 19.6 43 4.9
## 121 93.5 85.3 395.0 9.9 27.2 28 1.3
## 122 93.9 169.7 411.8 12.3 23.4 40 6.3
## 123 93.7 101.3 423.4 14.7 26.1 45 4.0
## 124 93.7 101.3 423.4 14.7 18.2 82 4.5
## 125 90.1 51.2 424.1 6.2 24.6 43 1.8
## 126 93.1 180.4 430.8 11.0 26.9 28 5.4
## 127 93.1 180.4 430.8 11.0 22.2 48 1.3
## 128 89.2 103.9 431.6 6.4 22.6 57 4.9
## 129 92.5 56.4 433.3 7.1 23.2 39 5.4
## 130 91.2 183.1 437.7 12.5 12.6 90 7.6
## 131 92.3 88.8 440.9 8.5 17.1 67 3.6
## 132 92.3 92.1 442.1 9.8 22.8 27 4.5
## 133 94.2 62.3 442.9 11.0 23.0 36 3.1
## 134 92.3 96.2 450.2 12.1 23.4 31 5.4
## 135 93.7 101.3 458.8 11.9 19.3 39 7.2
## 136 91.6 100.2 466.3 6.3 22.9 40 1.3
## 137 93.0 75.3 466.6 7.7 18.8 35 4.9
## 138 93.0 75.3 466.6 7.7 19.6 36 3.1
## 139 91.6 104.2 474.9 9.0 22.1 49 2.7
## 140 91.6 104.2 474.9 9.0 24.2 32 1.8
## 141 91.6 104.2 474.9 9.0 24.3 30 1.8
## 142 91.6 104.2 474.9 9.0 18.7 53 1.8
## 143 91.6 104.2 474.9 9.0 25.3 39 0.9
## 144 92.2 81.8 480.8 11.9 20.1 34 4.5
## 145 92.2 81.8 480.8 11.9 16.4 43 4.0
## 146 92.3 85.3 488.0 14.7 22.2 29 5.4
## 147 92.3 85.3 488.0 14.7 20.8 32 6.3
## 148 92.3 88.9 495.6 8.5 24.1 27 3.1
## 149 92.2 91.6 503.6 9.6 20.7 70 2.2
## 150 95.5 99.9 513.3 13.2 23.3 31 4.5
## 151 95.5 99.9 513.3 13.2 23.8 32 5.4
## 152 91.9 133.6 520.5 8.0 14.2 58 4.0
## 153 90.1 108.0 529.8 12.5 14.7 66 2.7
## 154 90.1 108.0 529.8 12.5 21.2 51 8.9
## 155 90.2 110.9 537.4 6.2 19.5 43 5.8
## 156 93.6 97.9 542.0 14.4 28.3 32 4.0
## 157 93.7 102.2 550.3 14.6 22.1 54 7.6
## 158 93.2 114.4 560.0 9.5 30.2 25 4.5
## 159 93.2 114.4 560.0 9.5 30.2 22 4.9
## 160 91.0 121.2 561.6 7.0 21.6 19 6.7
## 161 91.9 109.2 565.5 8.0 21.4 38 2.7
## 162 94.6 160.0 567.2 16.7 17.9 48 2.7
## 163 96.0 127.1 570.5 16.5 23.4 33 4.5
## 164 91.6 112.4 573.0 8.9 11.2 84 7.6
## 165 91.6 112.4 573.0 8.9 21.4 42 3.1
## 166 92.7 164.1 575.8 8.9 26.3 39 3.1
## 167 95.2 131.7 578.8 10.4 27.4 22 4.0
## 168 95.2 131.7 578.8 10.4 20.3 41 4.0
## 169 95.2 131.7 578.8 10.4 20.7 45 2.2
## 170 95.2 131.7 578.8 10.4 24.2 28 2.7
## 171 94.2 117.2 581.1 11.0 23.9 41 2.2
## 172 94.2 117.2 581.1 11.0 21.4 44 2.7
## 173 93.9 135.7 586.7 15.1 20.8 34 4.9
## 174 93.9 135.7 586.7 15.1 23.5 36 5.4
## 175 94.9 130.3 587.1 14.1 23.4 40 5.8
## 176 94.9 130.3 587.1 14.1 31.0 27 5.4
## 177 94.9 130.3 587.1 14.1 33.1 25 4.0
## 178 94.2 122.3 589.9 12.9 15.4 66 4.0
## 179 93.5 139.4 594.2 20.3 23.7 32 5.8
## 180 93.5 139.4 594.2 20.3 17.6 52 5.8
## 181 93.5 139.4 594.2 20.3 22.9 31 7.2
## 182 93.5 139.4 594.2 20.3 5.1 96 5.8
## 183 95.0 135.5 596.3 21.3 30.6 28 3.6
## 184 91.4 142.4 601.4 10.6 16.3 60 5.4
## 185 91.4 142.4 601.4 10.6 19.5 39 6.3
## 186 91.4 142.4 601.4 10.6 18.2 43 4.9
## 187 91.4 142.4 601.4 10.6 11.6 87 4.5
## 188 91.4 142.4 601.4 10.6 19.8 39 5.4
## 189 91.4 142.4 601.4 10.6 19.8 39 5.4
## 190 91.4 142.4 601.4 10.6 20.1 39 5.4
## 191 91.4 142.4 601.4 10.6 19.6 41 5.8
## 192 92.1 178.0 605.3 9.6 23.3 40 4.0
## 193 95.1 141.3 605.8 17.7 24.1 43 6.3
## 194 95.1 141.3 605.8 17.7 26.4 34 3.6
## 195 95.1 141.3 605.8 17.7 19.4 71 7.6
## 196 95.1 141.3 605.8 17.7 20.6 58 1.3
## 197 95.1 141.3 605.8 17.7 28.7 33 4.0
## 198 94.3 131.7 607.1 22.7 19.4 55 4.0
## 199 91.5 145.4 608.2 10.7 8.0 86 2.2
## 200 91.5 145.4 608.2 10.7 10.3 74 2.2
## 201 91.5 145.4 608.2 10.7 17.1 43 5.4
## 202 85.6 90.4 609.6 6.6 17.4 50 4.0
## 203 91.6 181.3 613.0 7.6 20.9 50 2.2
## 204 91.6 181.3 613.0 7.6 24.3 33 3.6
## 205 91.6 181.3 613.0 7.6 24.8 36 4.0
## 206 91.6 181.3 613.0 7.6 24.6 44 4.0
## 207 91.6 181.3 613.0 7.6 19.3 61 4.9
## 208 88.8 147.3 614.5 9.0 17.3 43 4.5
## 209 88.8 147.3 614.5 9.0 14.4 66 5.4
## 210 88.8 147.3 614.5 9.0 14.4 66 5.4
## 211 94.4 146.0 614.7 11.3 25.6 42 4.0
## 212 91.6 138.1 621.7 6.3 18.9 41 3.1
## 213 95.8 152.0 624.1 13.8 32.4 21 4.5
## 214 90.2 96.9 624.2 8.9 18.4 42 6.7
## 215 90.2 96.9 624.2 8.9 14.7 59 5.8
## 216 90.2 96.9 624.2 8.9 14.2 53 1.8
## 217 90.2 96.9 624.2 8.9 20.3 39 4.9
## 218 91.1 141.1 629.1 7.1 19.3 39 3.6
## 219 90.2 99.6 631.2 6.3 21.5 34 2.2
## 220 90.2 99.6 631.2 6.3 20.8 33 2.7
## 221 90.2 99.6 631.2 6.3 17.9 44 2.2
## 222 90.2 99.6 631.2 6.3 21.4 33 3.1
## 223 90.2 99.6 631.2 6.3 19.2 44 2.7
## 224 90.2 99.6 631.2 6.3 16.2 59 3.1
## 225 95.9 158.0 633.6 11.3 32.4 27 2.2
## 226 95.9 158.0 633.6 11.3 27.5 29 4.5
## 227 91.7 191.4 635.9 7.8 26.2 36 4.5
## 228 91.7 191.4 635.9 7.8 19.9 50 4.0
## 229 91.1 103.2 638.8 5.8 23.1 31 3.1
## 230 91.1 103.2 638.8 5.8 23.4 22 2.7
## 231 90.7 194.1 643.0 6.8 16.2 63 2.7
## 232 90.7 194.1 643.0 6.8 21.3 41 3.6
## 233 96.0 164.0 643.0 14.0 30.8 30 4.9
## 234 94.8 108.3 647.1 17.0 16.6 54 5.4
## 235 94.8 108.3 647.1 17.0 18.6 51 4.5
## 236 94.8 108.3 647.1 17.0 20.1 40 4.0
## 237 94.8 108.3 647.1 17.0 17.4 43 6.7
## 238 94.8 108.3 647.1 17.0 16.4 47 1.3
## 239 94.8 108.3 647.1 17.0 24.6 22 4.5
## 240 94.8 108.3 647.1 17.0 24.6 22 4.5
## 241 90.5 196.8 649.9 16.3 11.8 88 4.9
## 242 92.1 111.2 654.1 9.6 20.4 42 4.9
## 243 92.1 111.2 654.1 9.6 20.4 42 4.9
## 244 92.1 111.2 654.1 9.6 16.6 47 0.9
## 245 92.1 111.2 654.1 9.6 18.4 45 3.6
## 246 92.1 111.2 654.1 9.6 20.5 35 4.0
## 247 92.1 111.2 654.1 9.6 16.6 47 0.9
## 248 92.1 152.6 658.2 14.3 23.7 24 3.1
## 249 92.1 152.6 658.2 14.3 21.0 32 3.1
## 250 92.1 152.6 658.2 14.3 19.1 53 2.7
## 251 92.1 152.6 658.2 14.3 21.8 56 3.1
## 252 92.1 152.6 658.2 14.3 20.1 58 4.5
## 253 92.1 152.6 658.2 14.3 20.2 47 4.0
## 254 91.7 114.3 661.3 6.3 17.6 45 3.6
## 255 91.7 114.3 661.3 6.3 18.6 44 4.5
## 256 91.7 114.3 661.3 6.3 20.2 45 3.6
## 257 96.2 175.5 661.8 16.8 23.9 42 2.2
## 258 96.2 175.5 661.8 16.8 32.6 26 3.1
## 259 84.9 32.8 664.2 3.0 16.7 47 4.9
## 260 84.9 32.8 664.2 3.0 19.1 32 4.0
## 261 92.0 203.2 664.5 8.1 10.4 75 0.9
## 262 92.0 203.2 664.5 8.1 24.9 42 5.4
## 263 92.0 203.2 664.5 8.1 19.1 70 2.2
## 264 63.5 70.8 665.3 0.8 17.0 72 6.7
## 265 63.5 70.8 665.3 0.8 22.6 38 3.6
## 266 81.6 56.7 665.6 1.9 27.8 35 2.7
## 267 81.6 56.7 665.6 1.9 27.8 32 2.7
## 268 81.6 56.7 665.6 1.9 21.9 71 5.8
## 269 81.6 56.7 665.6 1.9 21.2 70 6.7
## 270 93.1 157.3 666.7 13.5 28.7 28 2.7
## 271 93.1 157.3 666.7 13.5 21.7 40 0.4
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## 284 0.0 1
## 285 0.4 0
## 286 0.8 0
## 287 0.8 0
## 288 6.4 1
## 289 0.0 0
## 290 0.0 1
## 291 0.0 1
## 292 0.0 0
## 293 0.0 1
## 294 0.0 1
## 295 0.0 1
## 296 0.0 0
## 297 0.0 1
## 298 0.0 0
## 299 0.0 1
## 300 0.0 1
## 301 0.0 1
## 302 0.0 1
## 303 0.0 0
## 304 0.0 0
## 305 0.0 1
## 306 0.0 1
## 307 0.0 0
## 308 0.0 0
## 309 0.0 1
## 310 0.0 0
## 311 0.0 1
## 312 0.0 1
## 313 0.0 0
## 314 0.0 0
## 315 0.0 0
## 316 0.0 0
## 317 0.0 1
## 318 0.0 1
## 319 0.0 1
## 320 0.0 0
## 321 0.0 0
## 322 0.0 1
## 323 0.0 1
## 324 0.0 1
## 325 0.0 1
## 326 0.0 0
## 327 0.0 1
## 328 0.0 0
## 329 0.0 0
## 330 0.0 0
## 331 0.0 0
## 332 0.0 0
## 333 0.0 0
## 334 0.0 0
## 335 0.0 1
## 336 0.0 1
## 337 0.0 0
## 338 0.0 1
## 339 0.0 0
## 340 0.0 0
## 341 0.0 1
## 342 0.0 1
## 343 0.0 1
## 344 0.0 1
## 345 0.0 1
## 346 0.0 1
## 347 0.0 1
## 348 0.0 0
## 349 0.0 1
## 350 0.0 1
## 351 0.0 0
## 352 0.0 0
## 353 0.0 0
## 354 0.0 0
## 355 0.0 0
## 356 0.0 1
## 357 0.0 0
## 358 0.0 0
## 359 0.0 0
## 360 0.0 1
## 361 0.0 1
## 362 0.0 1
## 363 0.0 1
## 364 0.0 1
## 365 0.0 1
## 366 0.0 1
## 367 1.0 0
## 368 0.0 0
## 369 0.0 0
## 370 0.0 1
## 371 0.0 0
## 372 0.0 0
## 373 0.0 0
## 374 0.0 0
## 375 0.0 0
## 376 0.0 1
## 377 0.0 1
## 378 0.0 0
## 379 0.0 1
## 380 0.0 0
## 381 0.0 0
## 382 0.0 0
## 383 0.0 0
## 384 0.0 0
## 385 0.0 1
## 386 0.0 0
## 387 0.0 0
## 388 0.0 0
## 389 0.0 1
## 390 0.0 0
## 391 0.0 1
## 392 0.0 1
## 393 0.0 1
## 394 0.0 0
## 395 0.0 0
## 396 0.0 1
## 397 0.0 1
## 398 0.0 1
## 399 0.0 0
## 400 0.0 0
## 401 0.0 0
## 402 0.0 1
## 403 0.0 0
## 404 0.0 1
## 405 0.0 1
## 406 0.0 0
## 407 0.0 1
## 408 0.0 0
## 409 0.0 0
## 410 0.0 0
## 411 0.0 1
## 412 0.0 1
## 413 0.0 0
## 414 0.0 0
## 415 0.0 0
## 416 0.0 1
## 417 0.0 1
## 418 0.0 1
## 419 0.0 0
## 420 0.0 0
## 421 0.0 0
## 422 0.0 1
## 423 0.0 1
## 424 0.0 0
## 425 0.0 1
## 426 0.0 1
## 427 0.0 1
## 428 0.0 1
## 429 0.0 0
## 430 0.0 1
## 431 0.0 1
## 432 0.0 1
## 433 0.0 1
## 434 0.0 1
## 435 0.0 0
## 436 0.0 0
## 437 0.0 1
## 438 0.0 1
## 439 0.0 1
## 440 0.0 1
## 441 0.0 1
## 442 0.0 1
## 443 0.0 1
## 444 0.0 1
## 445 0.0 1
## 446 0.0 1
## 447 0.0 0
## 448 0.0 0
## 449 0.0 0
## 450 0.0 0
## 451 0.0 1
## 452 0.0 1
## 453 0.0 0
## 454 0.0 0
## 455 0.0 0
## 456 0.0 1
## 457 1.4 1
## 458 0.0 1
## 459 0.0 0
## 460 0.0 0
## 461 0.0 0
## 462 0.0 0
## 463 0.0 0
## 464 0.0 0
## 465 0.0 0
## 466 0.0 1
## 467 0.0 0
## 468 0.0 0
## 469 0.0 0
## 470 0.0 1
## 471 0.0 1
## 472 0.0 1
## 473 0.0 1
## 474 0.0 0
## 475 0.0 0
## 476 0.0 1
## 477 0.0 1
## 478 0.0 1
## 479 0.0 0
## 480 0.0 1
## 481 0.0 0
## 482 0.0 1
## 483 0.0 1
## 484 0.0 1
## 485 0.0 1
## 486 0.0 0
## 487 0.0 0
## 488 0.0 1
## 489 0.0 1
## 490 0.0 1
## 491 0.0 1
## 492 0.0 1
## 493 0.0 1
## 494 0.0 0
## 495 0.0 0
## 496 0.0 1
## 497 0.0 1
## 498 0.0 1
## 499 0.0 1
## 500 0.0 0
## 501 0.0 1
## 502 0.0 0
## 503 0.0 0
## 504 0.0 1
## 505 0.0 1
## 506 0.0 1
## 507 0.0 0
## 508 0.0 1
## 509 0.0 1
## 510 0.0 1
## 511 0.0 0
## 512 0.0 0
## 513 0.0 1
## 514 0.0 1
## 515 0.0 0
## 516 0.0 1
## 517 0.0 1
set.seed(123)
index1 <- sample(1:nrow(ff3), round(nrow(ff3)*.8))
ff4 <- ff3[index1,]
dt <- rpart(ff4$fire__no_yes ~., method = 'class', data = ff4)
dt
## n= 414
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 414 204 1 (0.49275362 0.50724638)
## 2) temperature>=5.15 400 197 0 (0.50750000 0.49250000)
## 4) DC< 731.15 331 151 0 (0.54380665 0.45619335)
## 8) temperature< 21.65 220 85 0 (0.61363636 0.38636364)
## 16) ISI< 2.45 7 0 0 (1.00000000 0.00000000) *
## 17) ISI>=2.45 213 85 0 (0.60093897 0.39906103)
## 34) DMC>=141.85 37 10 0 (0.72972973 0.27027027) *
## 35) DMC< 141.85 176 75 0 (0.57386364 0.42613636)
## 70) DMC< 115.75 140 52 0 (0.62857143 0.37142857)
## 140) temperature>=13.75 96 29 0 (0.69791667 0.30208333)
## 280) DC< 79.15 13 0 0 (1.00000000 0.00000000) *
## 281) DC>=79.15 83 29 0 (0.65060241 0.34939759)
## 562) temperature>=17.2 56 15 0 (0.73214286 0.26785714) *
## 563) temperature< 17.2 27 13 1 (0.48148148 0.51851852)
## 1126) temperature< 16 14 4 0 (0.71428571 0.28571429) *
## 1127) temperature>=16 13 3 1 (0.23076923 0.76923077) *
## 141) temperature< 13.75 44 21 1 (0.47727273 0.52272727)
## 282) temperature< 12.35 36 16 0 (0.55555556 0.44444444)
## 564) ISI>=3.9 27 9 0 (0.66666667 0.33333333) *
## 565) ISI< 3.9 9 2 1 (0.22222222 0.77777778) *
## 283) temperature>=12.35 8 1 1 (0.12500000 0.87500000) *
## 71) DMC>=115.75 36 13 1 (0.36111111 0.63888889) *
## 9) temperature>=21.65 111 45 1 (0.40540541 0.59459459)
## 18) DMC>=209.55 11 3 0 (0.72727273 0.27272727) *
## 19) DMC< 209.55 100 37 1 (0.37000000 0.63000000)
## 38) relative humidity< 24.5 12 4 0 (0.66666667 0.33333333) *
## 39) relative humidity>=24.5 88 29 1 (0.32954545 0.67045455) *
## 5) DC>=731.15 69 23 1 (0.33333333 0.66666667)
## 10) DMC>=100.65 49 21 1 (0.42857143 0.57142857)
## 20) DC< 769.35 21 7 0 (0.66666667 0.33333333) *
## 21) DC>=769.35 28 7 1 (0.25000000 0.75000000) *
## 11) DMC< 100.65 20 2 1 (0.10000000 0.90000000) *
## 3) temperature< 5.15 14 1 1 (0.07142857 0.92857143) *
printcp(dt) # display the results
##
## Classification tree:
## rpart(formula = ff4$fire__no_yes ~ ., data = ff4, method = "class")
##
## Variables actually used in tree construction:
## [1] DC DMC ISI relative humidity
## [5] temperature
##
## Root node error: 204/414 = 0.49275
##
## n= 414
##
## CP nsplit rel error xerror xstd
## 1 0.071078 0 1.00000 1.07353 0.049786
## 2 0.024510 3 0.75490 0.88235 0.049444
## 3 0.019608 4 0.73039 0.90196 0.049561
## 4 0.017157 5 0.71078 0.91176 0.049613
## 5 0.016340 7 0.67647 0.93627 0.049721
## 6 0.011438 13 0.57353 0.90196 0.049561
## 7 0.010000 16 0.53922 0.90196 0.049561
plotcp(dt) # visualize cross-validation results
summary(dt)
## Call:
## rpart(formula = ff4$fire__no_yes ~ ., data = ff4, method = "class")
## n= 414
##
## CP nsplit rel error xerror xstd
## 1 0.07107843 0 1.0000000 1.0735294 0.04978621
## 2 0.02450980 3 0.7549020 0.8823529 0.04944403
## 3 0.01960784 4 0.7303922 0.9019608 0.04956128
## 4 0.01715686 5 0.7107843 0.9117647 0.04961278
## 5 0.01633987 7 0.6764706 0.9362745 0.04972088
## 6 0.01143791 13 0.5735294 0.9019608 0.04956128
## 7 0.01000000 16 0.5392157 0.9019608 0.04956128
##
## Variable importance
## temperature DMC DC FFMC
## 27 24 20 11
## ISI relative humidity wind speeds
## 10 6 1
##
## Node number 1: 414 observations, complexity param=0.07107843
## predicted class=1 expected loss=0.4927536 P(node) =1
## class counts: 204 210
## probabilities: 0.493 0.507
## left son=2 (400 obs) right son=3 (14 obs)
## Primary splits:
## temperature < 5.15 to the right, improve=5.144379, (0 missing)
## DC < 731.15 to the left, improve=4.208696, (0 missing)
## wind speeds < 8.25 to the left, improve=4.143089, (0 missing)
## relative humidity < 85 to the right, improve=2.393472, (0 missing)
## ISI < 1.65 to the left, improve=2.383862, (0 missing)
##
## Node number 2: 400 observations, complexity param=0.07107843
## predicted class=0 expected loss=0.4925 P(node) =0.9661836
## class counts: 203 197
## probabilities: 0.508 0.493
## left son=4 (331 obs) right son=5 (69 obs)
## Primary splits:
## DC < 731.15 to the left, improve=5.058726, (0 missing)
## DMC < 48.1 to the left, improve=4.455574, (0 missing)
## temperature < 21.75 to the left, improve=3.371667, (0 missing)
## relative humidity < 85 to the right, improve=3.160128, (0 missing)
## FFMC < 80.5 to the left, improve=2.205000, (0 missing)
## Surrogate splits:
## DMC < 243.3 to the left, agree=0.865, adj=0.217, (0 split)
##
## Node number 3: 14 observations
## predicted class=1 expected loss=0.07142857 P(node) =0.03381643
## class counts: 1 13
## probabilities: 0.071 0.929
##
## Node number 4: 331 observations, complexity param=0.07107843
## predicted class=0 expected loss=0.4561934 P(node) =0.7995169
## class counts: 180 151
## probabilities: 0.544 0.456
## left son=8 (220 obs) right son=9 (111 obs)
## Primary splits:
## temperature < 21.65 to the left, improve=6.397912, (0 missing)
## DMC < 99.75 to the left, improve=4.397195, (0 missing)
## relative humidity < 71.5 to the right, improve=3.179390, (0 missing)
## FFMC < 80.5 to the left, improve=1.798493, (0 missing)
## ISI < 1.7 to the left, improve=1.798493, (0 missing)
## Surrogate splits:
## FFMC < 92.65 to the left, agree=0.755, adj=0.270, (0 split)
## relative humidity < 36.5 to the right, agree=0.728, adj=0.189, (0 split)
## DMC < 148.55 to the left, agree=0.719, adj=0.162, (0 split)
## ISI < 12.95 to the left, agree=0.707, adj=0.126, (0 split)
## DC < 699.1 to the left, agree=0.674, adj=0.027, (0 split)
##
## Node number 5: 69 observations, complexity param=0.01715686
## predicted class=1 expected loss=0.3333333 P(node) =0.1666667
## class counts: 23 46
## probabilities: 0.333 0.667
## left son=10 (49 obs) right son=11 (20 obs)
## Primary splits:
## DMC < 100.65 to the right, improve=3.066667, (0 missing)
## DC < 751 to the right, improve=3.066667, (0 missing)
## wind speeds < 3.35 to the left, improve=2.904040, (0 missing)
## relative humidity < 27.5 to the left, improve=2.300000, (0 missing)
## FFMC < 92.15 to the right, improve=1.734540, (0 missing)
## Surrogate splits:
## DC < 751 to the right, agree=1.000, adj=1.00, (0 split)
## ISI < 8.55 to the left, agree=0.754, adj=0.15, (0 split)
## relative humidity < 25.5 to the right, agree=0.739, adj=0.10, (0 split)
##
## Node number 8: 220 observations, complexity param=0.01633987
## predicted class=0 expected loss=0.3863636 P(node) =0.531401
## class counts: 135 85
## probabilities: 0.614 0.386
## left son=16 (7 obs) right son=17 (213 obs)
## Primary splits:
## ISI < 2.45 to the left, improve=2.158557, (0 missing)
## DMC < 100.45 to the left, improve=1.658607, (0 missing)
## wind speeds < 7.8 to the left, improve=1.554936, (0 missing)
## relative humidity < 73.5 to the right, improve=1.528182, (0 missing)
## FFMC < 84.5 to the left, improve=1.493953, (0 missing)
## Surrogate splits:
## FFMC < 80.5 to the left, agree=0.995, adj=0.857, (0 split)
## DMC < 3.1 to the left, agree=0.982, adj=0.429, (0 split)
##
## Node number 9: 111 observations, complexity param=0.0245098
## predicted class=1 expected loss=0.4054054 P(node) =0.2681159
## class counts: 45 66
## probabilities: 0.405 0.595
## left son=18 (11 obs) right son=19 (100 obs)
## Primary splits:
## DMC < 209.55 to the right, improve=2.529877, (0 missing)
## DC < 689.55 to the right, improve=2.504862, (0 missing)
## relative humidity < 24.5 to the left, improve=2.424032, (0 missing)
## FFMC < 92.2 to the right, improve=1.653374, (0 missing)
## temperature < 22.15 to the right, improve=1.220786, (0 missing)
##
## Node number 10: 49 observations, complexity param=0.01715686
## predicted class=1 expected loss=0.4285714 P(node) =0.1183575
## class counts: 21 28
## probabilities: 0.429 0.571
## left son=20 (21 obs) right son=21 (28 obs)
## Primary splits:
## DC < 769.35 to the left, improve=4.166667, (0 missing)
## wind speeds < 3.8 to the left, improve=3.200000, (0 missing)
## DMC < 107.1 to the left, improve=1.975610, (0 missing)
## relative humidity < 27.5 to the left, improve=1.333333, (0 missing)
## ISI < 8.05 to the right, improve=0.800000, (0 missing)
## Surrogate splits:
## DMC < 110.05 to the left, agree=0.837, adj=0.619, (0 split)
## ISI < 6.4 to the left, agree=0.714, adj=0.333, (0 split)
## temperature < 22.15 to the right, agree=0.714, adj=0.333, (0 split)
## FFMC < 91.55 to the right, agree=0.673, adj=0.238, (0 split)
## wind speeds < 2.45 to the left, agree=0.673, adj=0.238, (0 split)
##
## Node number 11: 20 observations
## predicted class=1 expected loss=0.1 P(node) =0.04830918
## class counts: 2 18
## probabilities: 0.100 0.900
##
## Node number 16: 7 observations
## predicted class=0 expected loss=0 P(node) =0.01690821
## class counts: 7 0
## probabilities: 1.000 0.000
##
## Node number 17: 213 observations, complexity param=0.01633987
## predicted class=0 expected loss=0.399061 P(node) =0.5144928
## class counts: 128 85
## probabilities: 0.601 0.399
## left son=34 (37 obs) right son=35 (176 obs)
## Primary splits:
## DMC < 141.85 to the right, improve=1.4854840, (0 missing)
## wind speeds < 7.8 to the left, improve=1.4384040, (0 missing)
## DC < 57.1 to the right, improve=1.1001010, (0 missing)
## relative humidity < 38.5 to the left, improve=1.0793280, (0 missing)
## temperature < 13.75 to the right, improve=0.9185828, (0 missing)
## Surrogate splits:
## FFMC < 95.65 to the right, agree=0.840, adj=0.081, (0 split)
## rain amount < 0.1 to the right, agree=0.840, adj=0.081, (0 split)
## DC < 727.75 to the right, agree=0.831, adj=0.027, (0 split)
## relative humidity < 85 to the right, agree=0.831, adj=0.027, (0 split)
##
## Node number 18: 11 observations
## predicted class=0 expected loss=0.2727273 P(node) =0.02657005
## class counts: 8 3
## probabilities: 0.727 0.273
##
## Node number 19: 100 observations, complexity param=0.01960784
## predicted class=1 expected loss=0.37 P(node) =0.2415459
## class counts: 37 63
## probabilities: 0.370 0.630
## left son=38 (12 obs) right son=39 (88 obs)
## Primary splits:
## relative humidity < 24.5 to the left, improve=2.4003030, (0 missing)
## DC < 702.55 to the right, improve=1.4116670, (0 missing)
## FFMC < 94.45 to the left, improve=1.1491380, (0 missing)
## DMC < 152.3 to the left, improve=1.1266670, (0 missing)
## ISI < 16.9 to the left, improve=0.8753626, (0 missing)
##
## Node number 20: 21 observations
## predicted class=0 expected loss=0.3333333 P(node) =0.05072464
## class counts: 14 7
## probabilities: 0.667 0.333
##
## Node number 21: 28 observations
## predicted class=1 expected loss=0.25 P(node) =0.06763285
## class counts: 7 21
## probabilities: 0.250 0.750
##
## Node number 34: 37 observations
## predicted class=0 expected loss=0.2702703 P(node) =0.08937198
## class counts: 27 10
## probabilities: 0.730 0.270
##
## Node number 35: 176 observations, complexity param=0.01633987
## predicted class=0 expected loss=0.4261364 P(node) =0.4251208
## class counts: 101 75
## probabilities: 0.574 0.426
## left son=70 (140 obs) right son=71 (36 obs)
## Primary splits:
## DMC < 115.75 to the left, improve=4.097006, (0 missing)
## relative humidity < 38.5 to the left, improve=1.800974, (0 missing)
## FFMC < 94.1 to the left, improve=1.392045, (0 missing)
## wind speeds < 4.25 to the right, improve=1.263742, (0 missing)
## ISI < 10.15 to the left, improve=1.226347, (0 missing)
## Surrogate splits:
## FFMC < 94.95 to the left, agree=0.818, adj=0.111, (0 split)
## relative humidity < 21.5 to the right, agree=0.818, adj=0.111, (0 split)
## temperature < 21.45 to the left, agree=0.812, adj=0.083, (0 split)
## ISI < 17.35 to the left, agree=0.801, adj=0.028, (0 split)
##
## Node number 38: 12 observations
## predicted class=0 expected loss=0.3333333 P(node) =0.02898551
## class counts: 8 4
## probabilities: 0.667 0.333
##
## Node number 39: 88 observations
## predicted class=1 expected loss=0.3295455 P(node) =0.2125604
## class counts: 29 59
## probabilities: 0.330 0.670
##
## Node number 70: 140 observations, complexity param=0.01633987
## predicted class=0 expected loss=0.3714286 P(node) =0.3381643
## class counts: 88 52
## probabilities: 0.629 0.371
## left son=140 (96 obs) right son=141 (44 obs)
## Primary splits:
## temperature < 13.75 to the right, improve=2.937716, (0 missing)
## relative humidity < 52.5 to the left, improve=2.369292, (0 missing)
## wind speeds < 7.8 to the left, improve=1.732331, (0 missing)
## DC < 57.1 to the right, improve=1.678900, (0 missing)
## FFMC < 92.25 to the right, improve=1.540969, (0 missing)
## Surrogate splits:
## FFMC < 87.95 to the right, agree=0.800, adj=0.364, (0 split)
## DC < 156.9 to the right, agree=0.793, adj=0.341, (0 split)
## DMC < 25.3 to the right, agree=0.779, adj=0.295, (0 split)
## ISI < 5.6 to the right, agree=0.736, adj=0.159, (0 split)
## relative humidity < 68.5 to the left, agree=0.736, adj=0.159, (0 split)
##
## Node number 71: 36 observations
## predicted class=1 expected loss=0.3611111 P(node) =0.08695652
## class counts: 13 23
## probabilities: 0.361 0.639
##
## Node number 140: 96 observations, complexity param=0.01143791
## predicted class=0 expected loss=0.3020833 P(node) =0.2318841
## class counts: 67 29
## probabilities: 0.698 0.302
## left son=280 (13 obs) right son=281 (83 obs)
## Primary splits:
## DC < 79.15 to the left, improve=2.744227, (0 missing)
## DMC < 38.8 to the left, improve=2.300119, (0 missing)
## ISI < 7.75 to the left, improve=1.545600, (0 missing)
## temperature < 16 to the left, improve=1.173611, (0 missing)
## FFMC < 88.15 to the left, improve=1.108044, (0 missing)
## Surrogate splits:
## DMC < 30.35 to the left, agree=0.979, adj=0.846, (0 split)
## FFMC < 88.15 to the left, agree=0.917, adj=0.385, (0 split)
## ISI < 4.4 to the left, agree=0.906, adj=0.308, (0 split)
## temperature < 14.25 to the left, agree=0.896, adj=0.231, (0 split)
##
## Node number 141: 44 observations, complexity param=0.01633987
## predicted class=1 expected loss=0.4772727 P(node) =0.1062802
## class counts: 21 23
## probabilities: 0.477 0.523
## left son=282 (36 obs) right son=283 (8 obs)
## Primary splits:
## temperature < 12.35 to the left, improve=2.426768, (0 missing)
## DC < 57.1 to the right, improve=2.424631, (0 missing)
## relative humidity < 75.5 to the right, improve=2.402422, (0 missing)
## DMC < 18.7 to the right, improve=2.018913, (0 missing)
## ISI < 4 to the right, improve=1.227273, (0 missing)
## Surrogate splits:
## DC < 696.25 to the left, agree=0.841, adj=0.125, (0 split)
##
## Node number 280: 13 observations
## predicted class=0 expected loss=0 P(node) =0.03140097
## class counts: 13 0
## probabilities: 1.000 0.000
##
## Node number 281: 83 observations, complexity param=0.01143791
## predicted class=0 expected loss=0.3493976 P(node) =0.2004831
## class counts: 54 29
## probabilities: 0.651 0.349
## left son=562 (56 obs) right son=563 (27 obs)
## Primary splits:
## temperature < 17.2 to the right, improve=2.2891730, (0 missing)
## FFMC < 90.15 to the right, improve=1.2388610, (0 missing)
## ISI < 9.35 to the left, improve=1.1154590, (0 missing)
## DC < 697.35 to the right, improve=1.0390440, (0 missing)
## relative humidity < 42.5 to the left, improve=0.6673407, (0 missing)
## Surrogate splits:
## DMC < 60.5 to the right, agree=0.771, adj=0.296, (0 split)
## DC < 100.55 to the right, agree=0.747, adj=0.222, (0 split)
## relative humidity < 52.5 to the left, agree=0.711, adj=0.111, (0 split)
## wind speeds < 1.1 to the right, agree=0.711, adj=0.111, (0 split)
##
## Node number 282: 36 observations, complexity param=0.01633987
## predicted class=0 expected loss=0.4444444 P(node) =0.08695652
## class counts: 20 16
## probabilities: 0.556 0.444
## left son=564 (27 obs) right son=565 (9 obs)
## Primary splits:
## ISI < 3.9 to the right, improve=2.666667, (0 missing)
## DC < 60.8 to the right, improve=2.539683, (0 missing)
## DMC < 18.7 to the right, improve=2.500186, (0 missing)
## relative humidity < 71 to the right, improve=2.099206, (0 missing)
## FFMC < 85.05 to the right, improve=1.265463, (0 missing)
## Surrogate splits:
## FFMC < 85.15 to the right, agree=0.917, adj=0.667, (0 split)
## DMC < 11.15 to the right, agree=0.861, adj=0.444, (0 split)
## temperature < 6.25 to the right, agree=0.833, adj=0.333, (0 split)
## DC < 22.65 to the right, agree=0.778, adj=0.111, (0 split)
##
## Node number 283: 8 observations
## predicted class=1 expected loss=0.125 P(node) =0.01932367
## class counts: 1 7
## probabilities: 0.125 0.875
##
## Node number 562: 56 observations
## predicted class=0 expected loss=0.2678571 P(node) =0.1352657
## class counts: 41 15
## probabilities: 0.732 0.268
##
## Node number 563: 27 observations, complexity param=0.01143791
## predicted class=1 expected loss=0.4814815 P(node) =0.06521739
## class counts: 13 14
## probabilities: 0.481 0.519
## left son=1126 (14 obs) right son=1127 (13 obs)
## Primary splits:
## temperature < 16 to the left, improve=3.1518110, (0 missing)
## DMC < 48.4 to the left, improve=1.5167760, (0 missing)
## wind speeds < 2.9 to the left, improve=1.0243390, (0 missing)
## FFMC < 90.65 to the right, improve=0.9481481, (0 missing)
## ISI < 8.75 to the right, improve=0.8990639, (0 missing)
## Surrogate splits:
## DC < 627.7 to the left, agree=0.741, adj=0.462, (0 split)
## FFMC < 91.55 to the left, agree=0.667, adj=0.308, (0 split)
## DMC < 98.25 to the left, agree=0.667, adj=0.308, (0 split)
## ISI < 9.25 to the left, agree=0.667, adj=0.308, (0 split)
## relative humidity < 39 to the left, agree=0.630, adj=0.231, (0 split)
##
## Node number 564: 27 observations
## predicted class=0 expected loss=0.3333333 P(node) =0.06521739
## class counts: 18 9
## probabilities: 0.667 0.333
##
## Node number 565: 9 observations
## predicted class=1 expected loss=0.2222222 P(node) =0.02173913
## class counts: 2 7
## probabilities: 0.222 0.778
##
## Node number 1126: 14 observations
## predicted class=0 expected loss=0.2857143 P(node) =0.03381643
## class counts: 10 4
## probabilities: 0.714 0.286
##
## Node number 1127: 13 observations
## predicted class=1 expected loss=0.2307692 P(node) =0.03140097
## class counts: 3 10
## probabilities: 0.231 0.769
rpart.plot(dt, box.palette="RdBu", shadow.col="gray", nn=TRUE)
y_pred = predict(dt, newdata = ff4[,-9])
rfp <- as.data.frame(y_pred)
rfp$'0' <- as.factor(rfp$'0')
rfp$'1' <- as.factor(rfp$'1')
rfa <- as.data.frame(ff4[,9])
rfa$`fire__no_yes` <- as.factor(rfa$`ff4[, 9]`)
rfa$`fire__no_yes` <- as.factor(rfa$`fire__no_yes`)
length(rfa$`fire__no_yes`)
## [1] 414
length(rfp$y_pred)
## [1] 0
# confusionMatrix(rfp, rfa)
set.seed(1016)
index2 <- sample(1:nrow(ff3), round(nrow(ff3)*.8))
ff5 <- ff3[index2,]
dt <- rpart(ff5$fire__no_yes ~., method = 'class', data = ff5)
dt
## n= 414
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 414 198 1 (0.4782609 0.5217391)
## 2) DMC< 88.1 127 51 0 (0.5984252 0.4015748)
## 4) temperature>=5.15 117 42 0 (0.6410256 0.3589744)
## 8) relative humidity< 24.5 10 0 0 (1.0000000 0.0000000) *
## 9) relative humidity>=24.5 107 42 0 (0.6074766 0.3925234)
## 18) relative humidity>=71.5 9 0 0 (1.0000000 0.0000000) *
## 19) relative humidity< 71.5 98 42 0 (0.5714286 0.4285714)
## 38) relative humidity< 52.5 80 28 0 (0.6500000 0.3500000)
## 76) ISI< 5.65 24 3 0 (0.8750000 0.1250000) *
## 77) ISI>=5.65 56 25 0 (0.5535714 0.4464286)
## 154) DC>=433.5 27 7 0 (0.7407407 0.2592593) *
## 155) DC< 433.5 29 11 1 (0.3793103 0.6206897)
## 310) temperature< 12.4 9 3 0 (0.6666667 0.3333333) *
## 311) temperature>=12.4 20 5 1 (0.2500000 0.7500000) *
## 39) relative humidity>=52.5 18 4 1 (0.2222222 0.7777778) *
## 5) temperature< 5.15 10 1 1 (0.1000000 0.9000000) *
## 3) DMC>=88.1 287 122 1 (0.4250871 0.5749129)
## 6) ISI< 6.35 29 9 0 (0.6896552 0.3103448) *
## 7) ISI>=6.35 258 102 1 (0.3953488 0.6046512)
## 14) DMC>=133.45 130 62 1 (0.4769231 0.5230769)
## 28) temperature< 21.65 61 25 0 (0.5901639 0.4098361)
## 56) ISI>=7.85 43 13 0 (0.6976744 0.3023256)
## 112) ISI< 13.8 31 6 0 (0.8064516 0.1935484) *
## 113) ISI>=13.8 12 5 1 (0.4166667 0.5833333) *
## 57) ISI< 7.85 18 6 1 (0.3333333 0.6666667) *
## 29) temperature>=21.65 69 26 1 (0.3768116 0.6231884)
## 58) DC>=703.55 23 11 0 (0.5217391 0.4782609)
## 116) FFMC< 93.35 8 2 0 (0.7500000 0.2500000) *
## 117) FFMC>=93.35 15 6 1 (0.4000000 0.6000000) *
## 59) DC< 703.55 46 14 1 (0.3043478 0.6956522) *
## 15) DMC< 133.45 128 40 1 (0.3125000 0.6875000)
## 30) relative humidity< 27.5 26 13 0 (0.5000000 0.5000000)
## 60) FFMC< 92.85 16 5 0 (0.6875000 0.3125000) *
## 61) FFMC>=92.85 10 2 1 (0.2000000 0.8000000) *
## 31) relative humidity>=27.5 102 27 1 (0.2647059 0.7352941) *
printcp(dt) # display the results
##
## Classification tree:
## rpart(formula = ff5$fire__no_yes ~ ., data = ff5, method = "class")
##
## Variables actually used in tree construction:
## [1] DC DMC FFMC ISI
## [5] relative humidity temperature
##
## Root node error: 198/414 = 0.47826
##
## n= 414
##
## CP nsplit rel error xerror xstd
## 1 0.126263 0 1.00000 1.00000 0.051333
## 2 0.055556 1 0.87374 1.00505 0.051343
## 3 0.040404 2 0.81818 0.96465 0.051228
## 4 0.027778 3 0.77778 0.92424 0.051035
## 5 0.016835 6 0.69192 0.87879 0.050724
## 6 0.015152 11 0.60606 0.85354 0.050508
## 7 0.010101 14 0.56061 0.87374 0.050683
## 8 0.010000 17 0.53030 0.84848 0.050461
plotcp(dt) # visualize cross-validation results
summary(dt)
## Call:
## rpart(formula = ff5$fire__no_yes ~ ., data = ff5, method = "class")
## n= 414
##
## CP nsplit rel error xerror xstd
## 1 0.12626263 0 1.0000000 1.0000000 0.05133270
## 2 0.05555556 1 0.8737374 1.0050505 0.05134290
## 3 0.04040404 2 0.8181818 0.9646465 0.05122757
## 4 0.02777778 3 0.7777778 0.9242424 0.05103477
## 5 0.01683502 6 0.6919192 0.8787879 0.05072416
## 6 0.01515152 11 0.6060606 0.8535354 0.05050810
## 7 0.01010101 14 0.5606061 0.8737374 0.05068345
## 8 0.01000000 17 0.5303030 0.8484848 0.05046111
##
## Variable importance
## ISI FFMC DMC relative humidity
## 19 16 16 16
## temperature DC wind speeds
## 16 14 2
##
## Node number 1: 414 observations, complexity param=0.1262626
## predicted class=1 expected loss=0.4782609 P(node) =1
## class counts: 198 216
## probabilities: 0.478 0.522
## left son=2 (127 obs) right son=3 (287 obs)
## Primary splits:
## DMC < 88.1 to the left, improve=5.290580, (0 missing)
## temperature < 19.85 to the left, improve=4.832407, (0 missing)
## ISI < 6.35 to the left, improve=3.333932, (0 missing)
## wind speeds < 7.8 to the left, improve=3.260124, (0 missing)
## DC < 243.2 to the left, improve=3.209009, (0 missing)
## Surrogate splits:
## DC < 376.9 to the left, agree=0.867, adj=0.567, (0 split)
## FFMC < 88.25 to the left, agree=0.819, adj=0.409, (0 split)
## ISI < 5.75 to the left, agree=0.812, adj=0.386, (0 split)
## temperature < 15.3 to the left, agree=0.775, adj=0.268, (0 split)
## wind speeds < 7.8 to the right, agree=0.708, adj=0.047, (0 split)
##
## Node number 2: 127 observations, complexity param=0.04040404
## predicted class=0 expected loss=0.4015748 P(node) =0.3067633
## class counts: 76 51
## probabilities: 0.598 0.402
## left son=4 (117 obs) right son=5 (10 obs)
## Primary splits:
## temperature < 5.15 to the right, improve=5.393216, (0 missing)
## wind speeds < 7.8 to the left, improve=5.393216, (0 missing)
## relative humidity < 52.5 to the left, improve=3.159765, (0 missing)
## FFMC < 91.35 to the right, improve=3.010442, (0 missing)
## DC < 667.35 to the right, improve=2.859765, (0 missing)
## Surrogate splits:
## wind speeds < 8.25 to the left, agree=0.929, adj=0.1, (0 split)
##
## Node number 3: 287 observations, complexity param=0.05555556
## predicted class=1 expected loss=0.4250871 P(node) =0.6932367
## class counts: 122 165
## probabilities: 0.425 0.575
## left son=6 (29 obs) right son=7 (258 obs)
## Primary splits:
## ISI < 6.35 to the left, improve=4.516115, (0 missing)
## FFMC < 91.75 to the left, improve=2.614203, (0 missing)
## relative humidity < 55.5 to the right, improve=2.443404, (0 missing)
## DMC < 136.95 to the right, improve=2.216478, (0 missing)
## temperature < 19.85 to the left, improve=2.096131, (0 missing)
##
## Node number 4: 117 observations, complexity param=0.01683502
## predicted class=0 expected loss=0.3589744 P(node) =0.2826087
## class counts: 75 42
## probabilities: 0.641 0.359
## left son=8 (10 obs) right son=9 (107 obs)
## Primary splits:
## relative humidity < 24.5 to the left, improve=2.818116, (0 missing)
## DC < 667.35 to the right, improve=1.732183, (0 missing)
## FFMC < 91.35 to the right, improve=1.671571, (0 missing)
## DMC < 69.15 to the right, improve=1.466281, (0 missing)
## temperature < 23.1 to the left, improve=1.435613, (0 missing)
##
## Node number 5: 10 observations
## predicted class=1 expected loss=0.1 P(node) =0.02415459
## class counts: 1 9
## probabilities: 0.100 0.900
##
## Node number 6: 29 observations
## predicted class=0 expected loss=0.3103448 P(node) =0.07004831
## class counts: 20 9
## probabilities: 0.690 0.310
##
## Node number 7: 258 observations, complexity param=0.02777778
## predicted class=1 expected loss=0.3953488 P(node) =0.6231884
## class counts: 102 156
## probabilities: 0.395 0.605
## left son=14 (130 obs) right son=15 (128 obs)
## Primary splits:
## DMC < 133.45 to the right, improve=3.487299, (0 missing)
## relative humidity < 57.5 to the right, improve=3.002148, (0 missing)
## temperature < 12.7 to the left, improve=1.852902, (0 missing)
## DC < 499.6 to the left, improve=1.417553, (0 missing)
## FFMC < 89.9 to the left, improve=1.334851, (0 missing)
## Surrogate splits:
## FFMC < 92.85 to the right, agree=0.640, adj=0.273, (0 split)
## ISI < 10.5 to the right, agree=0.640, adj=0.273, (0 split)
## temperature < 21.65 to the right, agree=0.612, adj=0.219, (0 split)
## DC < 590.65 to the right, agree=0.605, adj=0.203, (0 split)
## relative humidity < 57.5 to the right, agree=0.578, adj=0.148, (0 split)
##
## Node number 8: 10 observations
## predicted class=0 expected loss=0 P(node) =0.02415459
## class counts: 10 0
## probabilities: 1.000 0.000
##
## Node number 9: 107 observations, complexity param=0.01683502
## predicted class=0 expected loss=0.3925234 P(node) =0.2584541
## class counts: 65 42
## probabilities: 0.607 0.393
## left son=18 (9 obs) right son=19 (98 obs)
## Primary splits:
## relative humidity < 71.5 to the right, improve=3.028037, (0 missing)
## temperature < 24.15 to the left, improve=2.209856, (0 missing)
## DMC < 69.15 to the right, improve=1.854704, (0 missing)
## DC < 667.35 to the right, improve=1.540696, (0 missing)
## ISI < 11.05 to the left, improve=1.087103, (0 missing)
## Surrogate splits:
## FFMC < 71.5 to the left, agree=0.944, adj=0.333, (0 split)
## ISI < 0.95 to the left, agree=0.944, adj=0.333, (0 split)
##
## Node number 14: 130 observations, complexity param=0.02777778
## predicted class=1 expected loss=0.4769231 P(node) =0.3140097
## class counts: 62 68
## probabilities: 0.477 0.523
## left son=28 (61 obs) right son=29 (69 obs)
## Primary splits:
## temperature < 21.65 to the left, improve=2.947545, (0 missing)
## relative humidity < 24.5 to the left, improve=2.701702, (0 missing)
## FFMC < 91.55 to the left, improve=2.165810, (0 missing)
## ISI < 12.85 to the left, improve=1.641042, (0 missing)
## DMC < 148.55 to the left, improve=1.400101, (0 missing)
## Surrogate splits:
## relative humidity < 42.5 to the right, agree=0.815, adj=0.607, (0 split)
## FFMC < 91.55 to the left, agree=0.769, adj=0.508, (0 split)
## ISI < 10.85 to the left, agree=0.669, adj=0.295, (0 split)
## DC < 729.6 to the right, agree=0.631, adj=0.213, (0 split)
## DMC < 233.1 to the right, agree=0.623, adj=0.197, (0 split)
##
## Node number 15: 128 observations, complexity param=0.01515152
## predicted class=1 expected loss=0.3125 P(node) =0.3091787
## class counts: 40 88
## probabilities: 0.312 0.688
## left son=30 (26 obs) right son=31 (102 obs)
## Primary splits:
## relative humidity < 27.5 to the left, improve=2.294118, (0 missing)
## FFMC < 92.85 to the left, improve=1.870635, (0 missing)
## DMC < 126.8 to the left, improve=1.848552, (0 missing)
## DC < 499.6 to the left, improve=1.657227, (0 missing)
## temperature < 15.75 to the left, improve=1.285714, (0 missing)
## Surrogate splits:
## temperature < 24 to the right, agree=0.883, adj=0.423, (0 split)
## DMC < 132 to the right, agree=0.812, adj=0.077, (0 split)
##
## Node number 18: 9 observations
## predicted class=0 expected loss=0 P(node) =0.02173913
## class counts: 9 0
## probabilities: 1.000 0.000
##
## Node number 19: 98 observations, complexity param=0.01683502
## predicted class=0 expected loss=0.4285714 P(node) =0.236715
## class counts: 56 42
## probabilities: 0.571 0.429
## left son=38 (80 obs) right son=39 (18 obs)
## Primary splits:
## relative humidity < 52.5 to the left, improve=5.377778, (0 missing)
## DC < 667.35 to the right, improve=2.136672, (0 missing)
## DMC < 69.15 to the right, improve=1.921039, (0 missing)
## temperature < 24.15 to the left, improve=1.800000, (0 missing)
## FFMC < 92.25 to the right, improve=1.602564, (0 missing)
## Surrogate splits:
## temperature < 6.65 to the right, agree=0.867, adj=0.278, (0 split)
## DMC < 9.2 to the right, agree=0.837, adj=0.111, (0 split)
## DC < 727.65 to the left, agree=0.837, adj=0.111, (0 split)
## FFMC < 83.95 to the right, agree=0.827, adj=0.056, (0 split)
##
## Node number 28: 61 observations, complexity param=0.02777778
## predicted class=0 expected loss=0.4098361 P(node) =0.147343
## class counts: 36 25
## probabilities: 0.590 0.410
## left son=56 (43 obs) right son=57 (18 obs)
## Primary splits:
## ISI < 7.85 to the right, improve=3.368662, (0 missing)
## DC < 818.3 to the left, improve=2.704560, (0 missing)
## wind speeds < 2.45 to the left, improve=2.296432, (0 missing)
## DMC < 263.7 to the left, improve=2.014079, (0 missing)
## FFMC < 91.15 to the right, improve=1.578372, (0 missing)
## Surrogate splits:
## DC < 729.6 to the left, agree=0.902, adj=0.667, (0 split)
## FFMC < 91.15 to the right, agree=0.852, adj=0.500, (0 split)
## DMC < 236.65 to the left, agree=0.836, adj=0.444, (0 split)
## wind speeds < 6.95 to the left, agree=0.738, adj=0.111, (0 split)
##
## Node number 29: 69 observations, complexity param=0.01010101
## predicted class=1 expected loss=0.3768116 P(node) =0.1666667
## class counts: 26 43
## probabilities: 0.377 0.623
## left son=58 (23 obs) right son=59 (46 obs)
## Primary splits:
## DC < 703.55 to the right, improve=1.4492750, (0 missing)
## FFMC < 92.8 to the right, improve=1.1639670, (0 missing)
## ISI < 9.05 to the right, improve=1.1617490, (0 missing)
## wind speeds < 4.7 to the left, improve=0.9057971, (0 missing)
## DMC < 209.55 to the right, improve=0.8371158, (0 missing)
## Surrogate splits:
## DMC < 224.7 to the right, agree=0.783, adj=0.348, (0 split)
## ISI < 9.35 to the left, agree=0.783, adj=0.348, (0 split)
## FFMC < 91.3 to the left, agree=0.710, adj=0.130, (0 split)
##
## Node number 30: 26 observations, complexity param=0.01515152
## predicted class=0 expected loss=0.5 P(node) =0.06280193
## class counts: 13 13
## probabilities: 0.500 0.500
## left son=60 (16 obs) right son=61 (10 obs)
## Primary splits:
## FFMC < 92.85 to the left, improve=2.925000, (0 missing)
## relative humidity < 26 to the right, improve=2.785714, (0 missing)
## wind speeds < 4.7 to the right, improve=2.443609, (0 missing)
## DC < 745.45 to the right, improve=2.124183, (0 missing)
## ISI < 8.55 to the left, improve=1.444444, (0 missing)
## Surrogate splits:
## ISI < 9 to the left, agree=0.885, adj=0.7, (0 split)
## temperature < 25.85 to the left, agree=0.846, adj=0.6, (0 split)
## DMC < 125.75 to the left, agree=0.808, adj=0.5, (0 split)
## DC < 719.5 to the right, agree=0.808, adj=0.5, (0 split)
## relative humidity < 23 to the right, agree=0.808, adj=0.5, (0 split)
##
## Node number 31: 102 observations
## predicted class=1 expected loss=0.2647059 P(node) =0.2463768
## class counts: 27 75
## probabilities: 0.265 0.735
##
## Node number 38: 80 observations, complexity param=0.01683502
## predicted class=0 expected loss=0.35 P(node) =0.1932367
## class counts: 52 28
## probabilities: 0.650 0.350
## left son=76 (24 obs) right son=77 (56 obs)
## Primary splits:
## ISI < 5.65 to the left, improve=3.471429, (0 missing)
## relative humidity < 29.5 to the right, improve=3.025000, (0 missing)
## temperature < 24.15 to the left, improve=2.844444, (0 missing)
## DMC < 35.6 to the left, improve=2.625455, (0 missing)
## FFMC < 88.95 to the left, improve=2.304762, (0 missing)
## Surrogate splits:
## FFMC < 88.35 to the left, agree=0.962, adj=0.875, (0 split)
## DMC < 29.05 to the left, agree=0.788, adj=0.292, (0 split)
## DC < 70.65 to the left, agree=0.775, adj=0.250, (0 split)
##
## Node number 39: 18 observations
## predicted class=1 expected loss=0.2222222 P(node) =0.04347826
## class counts: 4 14
## probabilities: 0.222 0.778
##
## Node number 56: 43 observations, complexity param=0.01010101
## predicted class=0 expected loss=0.3023256 P(node) =0.1038647
## class counts: 30 13
## probabilities: 0.698 0.302
## left son=112 (31 obs) right son=113 (12 obs)
## Primary splits:
## ISI < 13.8 to the left, improve=2.628782, (0 missing)
## FFMC < 92.05 to the left, improve=2.481526, (0 missing)
## temperature < 18.4 to the left, improve=2.263979, (0 missing)
## wind speeds < 2.45 to the left, improve=1.066808, (0 missing)
## DMC < 156.3 to the right, improve=1.055791, (0 missing)
## Surrogate splits:
## FFMC < 93.8 to the left, agree=0.860, adj=0.500, (0 split)
## DMC < 141.85 to the right, agree=0.767, adj=0.167, (0 split)
## rain amount < 0.3 to the left, agree=0.767, adj=0.167, (0 split)
## DC < 597.8 to the right, agree=0.744, adj=0.083, (0 split)
##
## Node number 57: 18 observations
## predicted class=1 expected loss=0.3333333 P(node) =0.04347826
## class counts: 6 12
## probabilities: 0.333 0.667
##
## Node number 58: 23 observations, complexity param=0.01010101
## predicted class=0 expected loss=0.4782609 P(node) =0.05555556
## class counts: 12 11
## probabilities: 0.522 0.478
## left son=116 (8 obs) right son=117 (15 obs)
## Primary splits:
## FFMC < 93.35 to the left, improve=1.278261, (0 missing)
## DC < 714.5 to the left, improve=1.278261, (0 missing)
## ISI < 8.8 to the right, improve=1.124415, (0 missing)
## wind speeds < 3.35 to the right, improve=1.124415, (0 missing)
## temperature < 26.55 to the left, improve=1.049689, (0 missing)
## Surrogate splits:
## DMC < 143.3 to the left, agree=0.870, adj=0.625, (0 split)
## DC < 706.55 to the left, agree=0.783, adj=0.375, (0 split)
## wind speeds < 2 to the left, agree=0.783, adj=0.375, (0 split)
## ISI < 7.6 to the left, agree=0.739, adj=0.250, (0 split)
## temperature < 22.6 to the left, agree=0.696, adj=0.125, (0 split)
##
## Node number 59: 46 observations
## predicted class=1 expected loss=0.3043478 P(node) =0.1111111
## class counts: 14 32
## probabilities: 0.304 0.696
##
## Node number 60: 16 observations
## predicted class=0 expected loss=0.3125 P(node) =0.03864734
## class counts: 11 5
## probabilities: 0.688 0.312
##
## Node number 61: 10 observations
## predicted class=1 expected loss=0.2 P(node) =0.02415459
## class counts: 2 8
## probabilities: 0.200 0.800
##
## Node number 76: 24 observations
## predicted class=0 expected loss=0.125 P(node) =0.05797101
## class counts: 21 3
## probabilities: 0.875 0.125
##
## Node number 77: 56 observations, complexity param=0.01683502
## predicted class=0 expected loss=0.4464286 P(node) =0.1352657
## class counts: 31 25
## probabilities: 0.554 0.446
## left son=154 (27 obs) right son=155 (29 obs)
## Primary splits:
## DC < 433.5 to the right, improve=3.653029, (0 missing)
## temperature < 23.1 to the left, improve=3.428571, (0 missing)
## FFMC < 91.35 to the right, improve=2.678571, (0 missing)
## relative humidity < 28.5 to the right, improve=2.159380, (0 missing)
## DMC < 83.5 to the right, improve=1.916955, (0 missing)
## Surrogate splits:
## DMC < 61.7 to the right, agree=0.893, adj=0.778, (0 split)
## FFMC < 91.75 to the right, agree=0.804, adj=0.593, (0 split)
## temperature < 17.3 to the right, agree=0.768, adj=0.519, (0 split)
## relative humidity < 31.5 to the right, agree=0.696, adj=0.370, (0 split)
## ISI < 10.45 to the right, agree=0.661, adj=0.296, (0 split)
##
## Node number 112: 31 observations
## predicted class=0 expected loss=0.1935484 P(node) =0.07487923
## class counts: 25 6
## probabilities: 0.806 0.194
##
## Node number 113: 12 observations
## predicted class=1 expected loss=0.4166667 P(node) =0.02898551
## class counts: 5 7
## probabilities: 0.417 0.583
##
## Node number 116: 8 observations
## predicted class=0 expected loss=0.25 P(node) =0.01932367
## class counts: 6 2
## probabilities: 0.750 0.250
##
## Node number 117: 15 observations
## predicted class=1 expected loss=0.4 P(node) =0.03623188
## class counts: 6 9
## probabilities: 0.400 0.600
##
## Node number 154: 27 observations
## predicted class=0 expected loss=0.2592593 P(node) =0.06521739
## class counts: 20 7
## probabilities: 0.741 0.259
##
## Node number 155: 29 observations, complexity param=0.01515152
## predicted class=1 expected loss=0.3793103 P(node) =0.07004831
## class counts: 11 18
## probabilities: 0.379 0.621
## left son=310 (9 obs) right son=311 (20 obs)
## Primary splits:
## temperature < 12.4 to the left, improve=2.155172, (0 missing)
## FFMC < 90.85 to the right, improve=1.998030, (0 missing)
## ISI < 7.4 to the right, improve=1.877395, (0 missing)
## wind speeds < 3.8 to the right, improve=1.877395, (0 missing)
## DMC < 37.75 to the left, improve=1.193634, (0 missing)
## Surrogate splits:
## FFMC < 89.45 to the left, agree=0.828, adj=0.444, (0 split)
## DMC < 19.75 to the left, agree=0.793, adj=0.333, (0 split)
## DC < 42.3 to the left, agree=0.793, adj=0.333, (0 split)
## relative humidity < 34.5 to the right, agree=0.759, adj=0.222, (0 split)
## wind speeds < 5.6 to the right, agree=0.759, adj=0.222, (0 split)
##
## Node number 310: 9 observations
## predicted class=0 expected loss=0.3333333 P(node) =0.02173913
## class counts: 6 3
## probabilities: 0.667 0.333
##
## Node number 311: 20 observations
## predicted class=1 expected loss=0.25 P(node) =0.04830918
## class counts: 5 15
## probabilities: 0.250 0.750
rpart.plot(dt, box.palette="RdBu", shadow.col="gray", nn=TRUE)
set.seed(69)
index3 <- sample(1:nrow(ff3), round(nrow(ff3)*.8))
ff6 <- ff3[index3,]
dt <- rpart(ff6$fire__no_yes ~., method = 'class', data = ff6)
dt
## n= 414
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 414 194 1 (0.4685990 0.5314010)
## 2) temperature< 19.85 227 105 0 (0.5374449 0.4625551)
## 4) wind speeds< 7.8 213 93 0 (0.5633803 0.4366197)
## 8) DC< 731.45 167 65 0 (0.6107784 0.3892216)
## 16) relative humidity< 38.5 42 9 0 (0.7857143 0.2142857) *
## 17) relative humidity>=38.5 125 56 0 (0.5520000 0.4480000)
## 34) DMC>=141.85 21 5 0 (0.7619048 0.2380952) *
## 35) DMC< 141.85 104 51 0 (0.5096154 0.4903846)
## 70) FFMC< 86.7 25 8 0 (0.6800000 0.3200000)
## 140) temperature>=10.35 12 0 0 (1.0000000 0.0000000) *
## 141) temperature< 10.35 13 5 1 (0.3846154 0.6153846) *
## 71) FFMC>=86.7 79 36 1 (0.4556962 0.5443038)
## 142) relative humidity>=47.5 43 19 0 (0.5581395 0.4418605)
## 284) relative humidity< 52.5 9 0 0 (1.0000000 0.0000000) *
## 285) relative humidity>=52.5 34 15 1 (0.4411765 0.5588235)
## 570) relative humidity>=57 24 10 0 (0.5833333 0.4166667)
## 1140) ISI>=6.65 16 5 0 (0.6875000 0.3125000) *
## 1141) ISI< 6.65 8 3 1 (0.3750000 0.6250000) *
## 571) relative humidity< 57 10 1 1 (0.1000000 0.9000000) *
## 143) relative humidity< 47.5 36 12 1 (0.3333333 0.6666667) *
## 9) DC>=731.45 46 18 1 (0.3913043 0.6086957)
## 18) ISI< 6.4 9 3 0 (0.6666667 0.3333333) *
## 19) ISI>=6.4 37 12 1 (0.3243243 0.6756757) *
## 5) wind speeds>=7.8 14 2 1 (0.1428571 0.8571429) *
## 3) temperature>=19.85 187 72 1 (0.3850267 0.6149733)
## 6) relative humidity< 24.5 14 3 0 (0.7857143 0.2142857) *
## 7) relative humidity>=24.5 173 61 1 (0.3526012 0.6473988)
## 14) temperature< 26.25 137 54 1 (0.3941606 0.6058394)
## 28) FFMC>=92.15 65 32 1 (0.4923077 0.5076923)
## 56) DMC>=168.65 15 3 0 (0.8000000 0.2000000) *
## 57) DMC< 168.65 50 20 1 (0.4000000 0.6000000)
## 114) FFMC< 92.85 20 7 0 (0.6500000 0.3500000) *
## 115) FFMC>=92.85 30 7 1 (0.2333333 0.7666667) *
## 29) FFMC< 92.15 72 22 1 (0.3055556 0.6944444) *
## 15) temperature>=26.25 36 7 1 (0.1944444 0.8055556) *
printcp(dt) # display the results
##
## Classification tree:
## rpart(formula = ff6$fire__no_yes ~ ., data = ff6, method = "class")
##
## Variables actually used in tree construction:
## [1] DC DMC FFMC ISI
## [5] relative humidity temperature wind speeds
##
## Root node error: 194/414 = 0.4686
##
## n= 414
##
## CP nsplit rel error xerror xstd
## 1 0.087629 0 1.00000 1.00000 0.052337
## 2 0.051546 1 0.91237 1.11340 0.052391
## 3 0.041237 3 0.80928 1.07216 0.052440
## 4 0.015464 4 0.76804 0.88144 0.051642
## 5 0.012027 9 0.67526 0.85567 0.051402
## 6 0.010309 16 0.55670 0.83505 0.051187
## 7 0.010000 17 0.54639 0.83505 0.051187
plotcp(dt) # visualize cross-validation results
summary(dt)
## Call:
## rpart(formula = ff6$fire__no_yes ~ ., data = ff6, method = "class")
## n= 414
##
## CP nsplit rel error xerror xstd
## 1 0.08762887 0 1.0000000 1.0000000 0.05233718
## 2 0.05154639 1 0.9123711 1.1134021 0.05239111
## 3 0.04123711 3 0.8092784 1.0721649 0.05244008
## 4 0.01546392 4 0.7680412 0.8814433 0.05164155
## 5 0.01202749 9 0.6752577 0.8556701 0.05140176
## 6 0.01030928 16 0.5567010 0.8350515 0.05118655
## 7 0.01000000 17 0.5463918 0.8350515 0.05118655
##
## Variable importance
## relative humidity temperature DMC FFMC
## 27 17 15 13
## DC ISI wind speeds rain amount
## 11 10 7 1
##
## Node number 1: 414 observations, complexity param=0.08762887
## predicted class=1 expected loss=0.468599 P(node) =1
## class counts: 194 220
## probabilities: 0.469 0.531
## left son=2 (227 obs) right son=3 (187 obs)
## Primary splits:
## temperature < 19.85 to the left, improve=4.763989, (0 missing)
## wind speeds < 7.8 to the left, improve=3.929806, (0 missing)
## DC < 243.2 to the left, improve=3.540300, (0 missing)
## DMC < 81.35 to the left, improve=3.538537, (0 missing)
## ISI < 1.7 to the left, improve=2.694659, (0 missing)
## Surrogate splits:
## FFMC < 92.85 to the left, agree=0.700, adj=0.337, (0 split)
## relative humidity < 42.5 to the right, agree=0.693, adj=0.321, (0 split)
## DMC < 99.3 to the left, agree=0.671, adj=0.273, (0 split)
## DC < 354.9 to the left, agree=0.650, adj=0.225, (0 split)
## ISI < 8.05 to the left, agree=0.645, adj=0.214, (0 split)
##
## Node number 2: 227 observations, complexity param=0.05154639
## predicted class=0 expected loss=0.4625551 P(node) =0.5483092
## class counts: 122 105
## probabilities: 0.537 0.463
## left son=4 (213 obs) right son=5 (14 obs)
## Primary splits:
## wind speeds < 7.8 to the left, improve=4.646132, (0 missing)
## temperature < 7.85 to the right, improve=4.215924, (0 missing)
## DC < 767.15 to the left, improve=3.099946, (0 missing)
## DMC < 141.85 to the right, improve=1.965279, (0 missing)
## relative humidity < 84 to the right, improve=1.889692, (0 missing)
## Surrogate splits:
## temperature < 5.15 to the right, agree=0.947, adj=0.143, (0 split)
## relative humidity < 22.5 to the right, agree=0.943, adj=0.071, (0 split)
##
## Node number 3: 187 observations, complexity param=0.04123711
## predicted class=1 expected loss=0.3850267 P(node) =0.4516908
## class counts: 72 115
## probabilities: 0.385 0.615
## left son=6 (14 obs) right son=7 (173 obs)
## Primary splits:
## relative humidity < 24.5 to the left, improve=4.859205, (0 missing)
## ISI < 17.8 to the left, improve=2.477937, (0 missing)
## temperature < 26 to the left, improve=1.864612, (0 missing)
## wind speeds < 3.8 to the left, improve=1.667494, (0 missing)
## DC < 613.85 to the right, improve=1.420892, (0 missing)
## Surrogate splits:
## DMC < 48 to the left, agree=0.936, adj=0.143, (0 split)
##
## Node number 4: 213 observations, complexity param=0.05154639
## predicted class=0 expected loss=0.4366197 P(node) =0.5144928
## class counts: 120 93
## probabilities: 0.563 0.437
## left son=8 (167 obs) right son=9 (46 obs)
## Primary splits:
## DC < 731.45 to the left, improve=3.474491, (0 missing)
## DMC < 81.35 to the left, improve=3.217227, (0 missing)
## relative humidity < 38.5 to the left, improve=3.205420, (0 missing)
## FFMC < 84.5 to the left, improve=1.806914, (0 missing)
## ISI < 1.95 to the left, improve=1.249204, (0 missing)
## Surrogate splits:
## DMC < 243.3 to the left, agree=0.831, adj=0.217, (0 split)
## temperature < 19.65 to the left, agree=0.793, adj=0.043, (0 split)
##
## Node number 5: 14 observations
## predicted class=1 expected loss=0.1428571 P(node) =0.03381643
## class counts: 2 12
## probabilities: 0.143 0.857
##
## Node number 6: 14 observations
## predicted class=0 expected loss=0.2142857 P(node) =0.03381643
## class counts: 11 3
## probabilities: 0.786 0.214
##
## Node number 7: 173 observations, complexity param=0.01546392
## predicted class=1 expected loss=0.3526012 P(node) =0.4178744
## class counts: 61 112
## probabilities: 0.353 0.647
## left son=14 (137 obs) right son=15 (36 obs)
## Primary splits:
## temperature < 26.25 to the left, improve=2.274224, (0 missing)
## ISI < 17.8 to the left, improve=2.085689, (0 missing)
## relative humidity < 26.5 to the right, improve=2.085689, (0 missing)
## wind speeds < 3.8 to the left, improve=1.395720, (0 missing)
## DC < 693.7 to the right, improve=1.143698, (0 missing)
## Surrogate splits:
## relative humidity < 30.5 to the right, agree=0.821, adj=0.139, (0 split)
## FFMC < 95.7 to the left, agree=0.809, adj=0.083, (0 split)
## DMC < 50.35 to the right, agree=0.803, adj=0.056, (0 split)
## ISI < 19 to the left, agree=0.803, adj=0.056, (0 split)
## DC < 323.95 to the right, agree=0.798, adj=0.028, (0 split)
##
## Node number 8: 167 observations, complexity param=0.01202749
## predicted class=0 expected loss=0.3892216 P(node) =0.4033816
## class counts: 102 65
## probabilities: 0.611 0.389
## left son=16 (42 obs) right son=17 (125 obs)
## Primary splits:
## relative humidity < 38.5 to the left, improve=3.434340, (0 missing)
## FFMC < 91.15 to the right, improve=1.771958, (0 missing)
## temperature < 7.85 to the right, improve=1.464489, (0 missing)
## DMC < 81.35 to the left, improve=1.418259, (0 missing)
## wind speeds < 4.25 to the right, improve=1.381895, (0 missing)
## Surrogate splits:
## DC < 29.25 to the left, agree=0.766, adj=0.071, (0 split)
##
## Node number 9: 46 observations, complexity param=0.01546392
## predicted class=1 expected loss=0.3913043 P(node) =0.1111111
## class counts: 18 28
## probabilities: 0.391 0.609
## left son=18 (9 obs) right son=19 (37 obs)
## Primary splits:
## ISI < 6.4 to the left, improve=1.6968270, (0 missing)
## relative humidity < 53.5 to the right, improve=1.1801000, (0 missing)
## DMC < 192.65 to the right, improve=1.1130430, (0 missing)
## DC < 748.4 to the right, improve=0.6915381, (0 missing)
## wind speeds < 3.8 to the left, improve=0.5899666, (0 missing)
## Surrogate splits:
## FFMC < 90.2 to the left, agree=0.848, adj=0.222, (0 split)
## DMC < 87.1 to the left, agree=0.848, adj=0.222, (0 split)
## DC < 736.9 to the left, agree=0.848, adj=0.222, (0 split)
##
## Node number 14: 137 observations, complexity param=0.01546392
## predicted class=1 expected loss=0.3941606 P(node) =0.3309179
## class counts: 54 83
## probabilities: 0.394 0.606
## left son=28 (65 obs) right son=29 (72 obs)
## Primary splits:
## FFMC < 92.15 to the right, improve=2.382794, (0 missing)
## relative humidity < 34.5 to the left, improve=2.373746, (0 missing)
## DMC < 144.2 to the right, improve=1.934265, (0 missing)
## wind speeds < 3.8 to the left, improve=1.892995, (0 missing)
## DC < 613.85 to the right, improve=1.660246, (0 missing)
## Surrogate splits:
## ISI < 8.35 to the right, agree=0.803, adj=0.585, (0 split)
## DMC < 114.85 to the right, agree=0.628, adj=0.215, (0 split)
## temperature < 22 to the right, agree=0.628, adj=0.215, (0 split)
## relative humidity < 32.5 to the left, agree=0.628, adj=0.215, (0 split)
## wind speeds < 3.8 to the right, agree=0.613, adj=0.185, (0 split)
##
## Node number 15: 36 observations
## predicted class=1 expected loss=0.1944444 P(node) =0.08695652
## class counts: 7 29
## probabilities: 0.194 0.806
##
## Node number 16: 42 observations
## predicted class=0 expected loss=0.2142857 P(node) =0.1014493
## class counts: 33 9
## probabilities: 0.786 0.214
##
## Node number 17: 125 observations, complexity param=0.01202749
## predicted class=0 expected loss=0.448 P(node) =0.3019324
## class counts: 69 56
## probabilities: 0.552 0.448
## left son=34 (21 obs) right son=35 (104 obs)
## Primary splits:
## DMC < 141.85 to the right, improve=2.2241830, (0 missing)
## temperature < 19.45 to the right, improve=1.3809010, (0 missing)
## relative humidity < 84.5 to the right, improve=1.3809010, (0 missing)
## FFMC < 91.15 to the right, improve=1.1218720, (0 missing)
## ISI < 3.45 to the left, improve=0.9318431, (0 missing)
## Surrogate splits:
## temperature < 19.45 to the right, agree=0.856, adj=0.143, (0 split)
##
## Node number 18: 9 observations
## predicted class=0 expected loss=0.3333333 P(node) =0.02173913
## class counts: 6 3
## probabilities: 0.667 0.333
##
## Node number 19: 37 observations
## predicted class=1 expected loss=0.3243243 P(node) =0.08937198
## class counts: 12 25
## probabilities: 0.324 0.676
##
## Node number 28: 65 observations, complexity param=0.01546392
## predicted class=1 expected loss=0.4923077 P(node) =0.1570048
## class counts: 32 33
## probabilities: 0.492 0.508
## left son=56 (15 obs) right son=57 (50 obs)
## Primary splits:
## DMC < 168.65 to the right, improve=3.692308, (0 missing)
## DC < 695.45 to the right, improve=2.862308, (0 missing)
## FFMC < 92.45 to the left, improve=1.954572, (0 missing)
## temperature < 23.85 to the right, improve=1.886247, (0 missing)
## ISI < 14.9 to the left, improve=1.728157, (0 missing)
## Surrogate splits:
## FFMC < 96.05 to the right, agree=0.815, adj=0.200, (0 split)
## relative humidity < 47.5 to the right, agree=0.815, adj=0.200, (0 split)
## rain amount < 0.2 to the right, agree=0.800, adj=0.133, (0 split)
## temperature < 25.75 to the right, agree=0.785, adj=0.067, (0 split)
##
## Node number 29: 72 observations
## predicted class=1 expected loss=0.3055556 P(node) =0.173913
## class counts: 22 50
## probabilities: 0.306 0.694
##
## Node number 34: 21 observations
## predicted class=0 expected loss=0.2380952 P(node) =0.05072464
## class counts: 16 5
## probabilities: 0.762 0.238
##
## Node number 35: 104 observations, complexity param=0.01202749
## predicted class=0 expected loss=0.4903846 P(node) =0.2512077
## class counts: 53 51
## probabilities: 0.510 0.490
## left son=70 (25 obs) right son=71 (79 obs)
## Primary splits:
## FFMC < 86.7 to the left, improve=1.910896, (0 missing)
## relative humidity < 41.5 to the right, improve=1.828595, (0 missing)
## DMC < 8.45 to the left, improve=1.812875, (0 missing)
## ISI < 3.45 to the left, improve=1.565624, (0 missing)
## temperature < 16 to the left, improve=1.538490, (0 missing)
## Surrogate splits:
## ISI < 3.6 to the left, agree=0.942, adj=0.76, (0 split)
## DMC < 33.05 to the left, agree=0.894, adj=0.56, (0 split)
## temperature < 8.25 to the left, agree=0.856, adj=0.40, (0 split)
## DC < 61.5 to the left, agree=0.837, adj=0.32, (0 split)
##
## Node number 56: 15 observations
## predicted class=0 expected loss=0.2 P(node) =0.03623188
## class counts: 12 3
## probabilities: 0.800 0.200
##
## Node number 57: 50 observations, complexity param=0.01546392
## predicted class=1 expected loss=0.4 P(node) =0.1207729
## class counts: 20 30
## probabilities: 0.400 0.600
## left son=114 (20 obs) right son=115 (30 obs)
## Primary splits:
## FFMC < 92.85 to the left, improve=4.166667, (0 missing)
## ISI < 9 to the left, improve=3.841270, (0 missing)
## DC < 717.5 to the right, improve=3.020979, (0 missing)
## DMC < 121.4 to the left, improve=1.500000, (0 missing)
## relative humidity < 32.5 to the left, improve=1.500000, (0 missing)
## Surrogate splits:
## ISI < 9 to the left, agree=0.80, adj=0.50, (0 split)
## DC < 740.05 to the right, agree=0.78, adj=0.45, (0 split)
## DMC < 121.4 to the left, agree=0.76, adj=0.40, (0 split)
## relative humidity < 30 to the left, agree=0.76, adj=0.40, (0 split)
## temperature < 21.65 to the left, agree=0.64, adj=0.10, (0 split)
##
## Node number 70: 25 observations, complexity param=0.01202749
## predicted class=0 expected loss=0.32 P(node) =0.06038647
## class counts: 17 8
## probabilities: 0.680 0.320
## left son=140 (12 obs) right son=141 (13 obs)
## Primary splits:
## temperature < 10.35 to the right, improve=4.7261540, (0 missing)
## relative humidity < 52.5 to the left, improve=4.7261540, (0 missing)
## wind speeds < 4.25 to the left, improve=1.9968830, (0 missing)
## DMC < 18.85 to the right, improve=0.7501299, (0 missing)
## DC < 72.75 to the right, improve=0.7501299, (0 missing)
## Surrogate splits:
## relative humidity < 49.5 to the left, agree=0.80, adj=0.583, (0 split)
## DMC < 30.15 to the right, agree=0.68, adj=0.333, (0 split)
## wind speeds < 3.55 to the left, agree=0.68, adj=0.333, (0 split)
## DC < 508.85 to the right, agree=0.64, adj=0.250, (0 split)
## FFMC < 71.65 to the left, agree=0.60, adj=0.167, (0 split)
##
## Node number 71: 79 observations, complexity param=0.01202749
## predicted class=1 expected loss=0.4556962 P(node) =0.1908213
## class counts: 36 43
## probabilities: 0.456 0.544
## left son=142 (43 obs) right son=143 (36 obs)
## Primary splits:
## relative humidity < 47.5 to the right, improve=1.9805710, (0 missing)
## DC < 100.55 to the right, improve=0.9832800, (0 missing)
## temperature < 16 to the left, improve=0.9548085, (0 missing)
## wind speeds < 4.25 to the right, improve=0.8767014, (0 missing)
## DMC < 128 to the left, improve=0.5550908, (0 missing)
## Surrogate splits:
## DC < 674.1 to the right, agree=0.608, adj=0.139, (0 split)
## temperature < 18.3 to the left, agree=0.608, adj=0.139, (0 split)
## FFMC < 87.75 to the right, agree=0.582, adj=0.083, (0 split)
## ISI < 9.55 to the left, agree=0.582, adj=0.083, (0 split)
## wind speeds < 1.1 to the right, agree=0.582, adj=0.083, (0 split)
##
## Node number 114: 20 observations
## predicted class=0 expected loss=0.35 P(node) =0.04830918
## class counts: 13 7
## probabilities: 0.650 0.350
##
## Node number 115: 30 observations
## predicted class=1 expected loss=0.2333333 P(node) =0.07246377
## class counts: 7 23
## probabilities: 0.233 0.767
##
## Node number 140: 12 observations
## predicted class=0 expected loss=0 P(node) =0.02898551
## class counts: 12 0
## probabilities: 1.000 0.000
##
## Node number 141: 13 observations
## predicted class=1 expected loss=0.3846154 P(node) =0.03140097
## class counts: 5 8
## probabilities: 0.385 0.615
##
## Node number 142: 43 observations, complexity param=0.01202749
## predicted class=0 expected loss=0.4418605 P(node) =0.1038647
## class counts: 24 19
## probabilities: 0.558 0.442
## left son=284 (9 obs) right son=285 (34 obs)
## Primary splits:
## relative humidity < 52.5 to the left, improve=4.4445960, (0 missing)
## FFMC < 90.8 to the right, improve=2.0015100, (0 missing)
## ISI < 5.6 to the right, improve=1.8664450, (0 missing)
## DMC < 104.65 to the right, improve=1.0122580, (0 missing)
## DC < 639.15 to the right, improve=0.9668781, (0 missing)
## Surrogate splits:
## temperature < 18.4 to the right, agree=0.837, adj=0.222, (0 split)
## DMC < 21.9 to the left, agree=0.814, adj=0.111, (0 split)
## DC < 45.9 to the left, agree=0.814, adj=0.111, (0 split)
##
## Node number 143: 36 observations
## predicted class=1 expected loss=0.3333333 P(node) =0.08695652
## class counts: 12 24
## probabilities: 0.333 0.667
##
## Node number 284: 9 observations
## predicted class=0 expected loss=0 P(node) =0.02173913
## class counts: 9 0
## probabilities: 1.000 0.000
##
## Node number 285: 34 observations, complexity param=0.01202749
## predicted class=1 expected loss=0.4411765 P(node) =0.0821256
## class counts: 15 19
## probabilities: 0.441 0.559
## left son=570 (24 obs) right son=571 (10 obs)
## Primary splits:
## relative humidity < 57 to the right, improve=3.2980390, (0 missing)
## ISI < 6 to the right, improve=2.0916290, (0 missing)
## DMC < 104.65 to the right, improve=1.2390140, (0 missing)
## FFMC < 90.25 to the right, improve=1.0008170, (0 missing)
## temperature < 17.55 to the left, improve=0.7647059, (0 missing)
## Surrogate splits:
## FFMC < 94.15 to the left, agree=0.735, adj=0.1, (0 split)
## DC < 712.1 to the left, agree=0.735, adj=0.1, (0 split)
## ISI < 15.85 to the left, agree=0.735, adj=0.1, (0 split)
##
## Node number 570: 24 observations, complexity param=0.01030928
## predicted class=0 expected loss=0.4166667 P(node) =0.05797101
## class counts: 14 10
## probabilities: 0.583 0.417
## left son=1140 (16 obs) right son=1141 (8 obs)
## Primary splits:
## ISI < 6.65 to the right, improve=1.0416670, (0 missing)
## temperature < 14.9 to the left, improve=0.6736597, (0 missing)
## DMC < 104.65 to the right, improve=0.6666667, (0 missing)
## DC < 683.6 to the right, improve=0.6666667, (0 missing)
## FFMC < 89.9 to the right, improve=0.4733894, (0 missing)
## Surrogate splits:
## FFMC < 88.4 to the right, agree=0.792, adj=0.375, (0 split)
## DMC < 90.9 to the right, agree=0.750, adj=0.250, (0 split)
## DC < 720.05 to the left, agree=0.750, adj=0.250, (0 split)
## temperature < 11.05 to the right, agree=0.708, adj=0.125, (0 split)
##
## Node number 571: 10 observations
## predicted class=1 expected loss=0.1 P(node) =0.02415459
## class counts: 1 9
## probabilities: 0.100 0.900
##
## Node number 1140: 16 observations
## predicted class=0 expected loss=0.3125 P(node) =0.03864734
## class counts: 11 5
## probabilities: 0.688 0.312
##
## Node number 1141: 8 observations
## predicted class=1 expected loss=0.375 P(node) =0.01932367
## class counts: 3 5
## probabilities: 0.375 0.625
rpart.plot(dt, box.palette="RdBu", shadow.col="gray", nn=TRUE)
#### Random Forest
traindata2 <- dplyr::select(traindata,(1:9))
testdata2 <- testdata
colnames(traindata2) <- c("FFMC","DMC","DC", "ISI","Temp","Rel_Hum","Wind_Speed","Rain_Amt","Y/N")
colnames(testdata2) <- c("FFMC","DMC","DC", "ISI","Temp","Rel_Hum","Wind_Speed","Rain_Amt","Y/N")
traindata2$`Y/N` <- as.numeric(traindata2$`Y/N`)
traindata2 <- as.data.frame(traindata2)
Random_Forest <- randomForest(formula = traindata2$`Y/N` ~ .,data=traindata2,ntree = 400, mtry = 6, importance = TRUE)
Random_Forest
##
## Call:
## randomForest(formula = traindata2$`Y/N` ~ ., data = traindata2, ntree = 400, mtry = 6, importance = TRUE)
## Type of random forest: regression
## Number of trees: 400
## No. of variables tried at each split: 6
##
## Mean of squared residuals: 2.468835
## % Var explained: 18.67
plot(Random_Forest)
gt <- getTree(Random_Forest, 5, labelVar=TRUE)
gt <- as.data.frame(gt)
summary(gt)
## left daughter right daughter split var split point
## Min. : 0.00 Min. : 0.00 Temp : 20 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00 Rel_Hum : 19 1st Qu.: 0.00
## Median : 0.00 Median : 0.00 Wind_Speed: 19 Median : 0.00
## Mean : 58.75 Mean : 59.25 ISI : 18 Mean : 57.10
## 3rd Qu.:117.00 3rd Qu.:118.00 Rain_Amt : 15 3rd Qu.: 29.65
## Max. :234.00 Max. :235.00 (Other) : 26 Max. :811.65
## NA's :118
## status prediction
## Min. :-3.000 Min. :0.900
## 1st Qu.:-3.000 1st Qu.:2.836
## Median :-1.000 Median :4.000
## Mean :-1.996 Mean :4.042
## 3rd Qu.:-1.000 3rd Qu.:4.940
## Max. :-1.000 Max. :9.400
##
y_pred = predict(Random_Forest, newdata = testdata2[,-9])
rfp <- as.data.frame(y_pred)
rfp$y_pred <- as.factor(rfp$y_pred)
rfa <- as.data.frame(testdata2[,9])
rfa$`Y/N` <- as.factor(rfa$`Y/N`)
length(rfa$`Y/N`)
## [1] 171
length(rfp$y_pred)
## [1] 171
rfa$`Y/N` <- as.factor(rfa$`Y/N`)
rfp$y_pred <- as.factor(rfp$y_pred)
#confusionMatrix(rfp$y_pred, rfa$`Y/N`)
hist(ff3$fire__no_yes)
ggplot(ff3, aes(x=ff3$fire__no_yes)) + geom_histogram(binwidth=0.4) + labs(title = "Severe Fire Yes or No", x="No Yes", y="count")
# Naive Bayes
ff <- ForestFiresWith
#ff <-as.data.frame(read.csv("C:/Users/tmacd/Downloads/fire.csv"))
#ff<-read.csv("C:/Users/tmacd/Downloads/fire.csv")
#ff <-as.data.frame(ff)
#fff<-ff %>% mutate_if(is.numeric,funs(as.factor))
#str(ff)
#corrplot(ff, method = "number")
#corrplot(corrgram(ff))
str(ff)
## Classes 'tbl_df', 'tbl' and 'data.frame': 517 obs. of 14 variables:
## $ X : num 7 2 2 3 5 6 6 3 2 6 ...
## $ Y : num 5 4 2 4 4 5 4 4 4 3 ...
## $ month : chr "apr" "jan" "feb" "mar" ...
## $ day : chr "sun" "sat" "sat" "sat" ...
## $ FFMC : num 81.9 82.1 79.5 69 85.2 75.1 75.1 86.9 93.4 91 ...
## $ DMC : num 3 3.7 3.6 2.4 4.9 4.4 4.4 6.6 15 14.6 ...
## $ DC : num 7.9 9.3 15.3 15.5 15.8 16.2 16.2 18.7 25.6 25.6 ...
## $ ISI : num 3.5 2.9 1.8 0.7 6.3 1.9 1.9 3.2 11.4 12.3 ...
## $ temperature : num 13.4 5.3 4.6 17.4 7.5 4.6 5.1 8.8 15.2 13.7 ...
## $ relative humidity: num 75 78 59 24 46 82 77 35 19 33 ...
## $ wind speeds : num 1.8 3.1 0.9 5.4 8 6.3 5.4 3.1 7.6 9.4 ...
## $ rain amount : num 0 0 0 0 0 0 0 0 0 0 ...
## $ area : num 0 0 6.84 0 24.24 ...
## $ fire__no_yes : num 0 0 1 0 1 1 1 1 0 1 ...
ff$month <- as.factor(ff$month)
ff$day <- as.factor(ff$day)
ff$fire__no_yes <- as.factor(ff$fire__no_yes)
ff <- japply( ff, which(sapply(ff, class)=="integer"), as.numeric )
str(ff)
## Classes 'tbl_df', 'tbl' and 'data.frame': 517 obs. of 14 variables:
## $ X : num 7 2 2 3 5 6 6 3 2 6 ...
## $ Y : num 5 4 2 4 4 5 4 4 4 3 ...
## $ month : Factor w/ 12 levels "apr","aug","dec",..: 1 5 4 8 4 4 4 4 8 1 ...
## $ day : Factor w/ 7 levels "fri","mon","sat",..: 4 3 3 3 1 6 6 7 6 4 ...
## $ FFMC : num 81.9 82.1 79.5 69 85.2 75.1 75.1 86.9 93.4 91 ...
## $ DMC : num 3 3.7 3.6 2.4 4.9 4.4 4.4 6.6 15 14.6 ...
## $ DC : num 7.9 9.3 15.3 15.5 15.8 16.2 16.2 18.7 25.6 25.6 ...
## $ ISI : num 3.5 2.9 1.8 0.7 6.3 1.9 1.9 3.2 11.4 12.3 ...
## $ temperature : num 13.4 5.3 4.6 17.4 7.5 4.6 5.1 8.8 15.2 13.7 ...
## $ relative humidity: num 75 78 59 24 46 82 77 35 19 33 ...
## $ wind speeds : num 1.8 3.1 0.9 5.4 8 6.3 5.4 3.1 7.6 9.4 ...
## $ rain amount : num 0 0 0 0 0 0 0 0 0 0 ...
## $ area : num 0 0 6.84 0 24.24 ...
## $ fire__no_yes : Factor w/ 2 levels "0","1": 1 1 2 1 2 2 2 2 1 2 ...
numff<-ff[,-c(1,2,3,4)]
str(numff)
## Classes 'tbl_df', 'tbl' and 'data.frame': 517 obs. of 10 variables:
## $ FFMC : num 81.9 82.1 79.5 69 85.2 75.1 75.1 86.9 93.4 91 ...
## $ DMC : num 3 3.7 3.6 2.4 4.9 4.4 4.4 6.6 15 14.6 ...
## $ DC : num 7.9 9.3 15.3 15.5 15.8 16.2 16.2 18.7 25.6 25.6 ...
## $ ISI : num 3.5 2.9 1.8 0.7 6.3 1.9 1.9 3.2 11.4 12.3 ...
## $ temperature : num 13.4 5.3 4.6 17.4 7.5 4.6 5.1 8.8 15.2 13.7 ...
## $ relative humidity: num 75 78 59 24 46 82 77 35 19 33 ...
## $ wind speeds : num 1.8 3.1 0.9 5.4 8 6.3 5.4 3.1 7.6 9.4 ...
## $ rain amount : num 0 0 0 0 0 0 0 0 0 0 ...
## $ area : num 0 0 6.84 0 24.24 ...
## $ fire__no_yes : Factor w/ 2 levels "0","1": 1 1 2 1 2 2 2 2 1 2 ...
ggpairs(numff)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#testdata$fire__no_yes <- as.factor(testdata$fire__no_yes)
x<-traindata[ , -which(names(traindata) %in% c("fire__no_yes"))]
str(x)
## Classes 'tbl_df', 'tbl' and 'data.frame': 346 obs. of 10 variables:
## $ X : num 0.693 1.386 1.099 1.099 1.792 ...
## $ Y : num 2 3 4 5 5 6 4 4 4 4 ...
## $ FFMC : num 84 90.3 91.8 93.5 87.1 91.1 91.9 91.7 91.5 92.1 ...
## $ DMC : num 9.3 290 170.9 139.4 291.3 ...
## $ DC : num 34 855 692 594 861 ...
## $ ISI : num 2.1 7.4 13.7 20.3 4 5.8 8 7.8 10.7 9.6 ...
## $ temperature : num 13.9 19.9 20.6 17.6 17 23.4 21.4 17 17.1 17.4 ...
## $ relative humidity: num 40 44 59 52 67 22 38 27 43 57 ...
## $ wind speeds : num 5.4 3.1 0.9 5.8 4.9 2.7 2.7 4.9 5.4 4.5 ...
## $ rain amount : num 0 0 0 0 0 0 0 0 0 0 ...
y <- traindata[,"fire__no_yes"]
str(y)
## Classes 'tbl_df', 'tbl' and 'data.frame': 346 obs. of 1 variable:
## $ fire__no_yes: num 0 1 0 0 1 0 1 1 0 0 ...
##remove "area" column.
ff <- ff[,-13]
#str(ff)
#sapply(ff, sd)
trainRatio <- .67
set.seed(1016) # Set Seed so that same sample can be reproduced in future also
sample <- sample.int(n = nrow(ff), size = floor(trainRatio*nrow(ff)), replace = FALSE)
testdata <- ff[-sample, ]
str(testdata)
## Classes 'tbl_df', 'tbl' and 'data.frame': 171 obs. of 13 variables:
## $ X : num 7 6 3 6 3 5 6 2 6 4 ...
## $ Y : num 5 5 4 3 4 5 5 2 5 5 ...
## $ month : Factor w/ 12 levels "apr","aug","dec",..: 1 4 4 1 4 8 8 4 8 4 ...
## $ day : Factor w/ 7 levels "fri","mon","sat",..: 4 6 7 4 3 5 2 1 5 4 ...
## $ FFMC : num 81.9 75.1 86.9 91 83.9 90.9 87.2 86.6 91.3 85 ...
## $ DMC : num 3 4.4 6.6 14.6 8 18.9 15.1 13.2 20.6 9 ...
## $ DC : num 7.9 16.2 18.7 25.6 30.2 30.6 36.9 43 43.5 56.9 ...
## $ ISI : num 3.5 1.9 3.2 12.3 2.6 8 7.1 5.3 8.5 3.5 ...
## $ temperature : num 13.4 4.6 8.8 13.7 12.7 11.6 10.2 12.3 13.3 10.1 ...
## $ relative humidity: num 75 82 35 33 48 48 45 51 27 62 ...
## $ wind speeds : num 1.8 6.3 3.1 9.4 1.8 5.4 5.8 0.9 3.6 1.8 ...
## $ rain amount : num 0 0 0 0 0 0 0 0 0 0 ...
## $ fire__no_yes : Factor w/ 2 levels "0","1": 1 2 2 2 1 1 2 1 2 2 ...
testdata <- testdata[, -c(1:4)]
summary(testdata)
## FFMC DMC DC ISI
## Min. :50.40 Min. : 3.00 Min. : 7.9 Min. : 0.400
## 1st Qu.:90.10 1st Qu.: 51.75 1st Qu.:399.9 1st Qu.: 6.700
## Median :91.60 Median : 97.90 Median :664.5 Median : 8.400
## Mean :90.48 Mean :100.55 Mean :536.5 Mean : 8.763
## 3rd Qu.:92.50 3rd Qu.:130.90 3rd Qu.:713.5 3rd Qu.:10.100
## Max. :96.10 Max. :276.30 Max. :825.1 Max. :22.600
## temperature relative humidity wind speeds rain amount
## Min. : 4.60 Min. :17.00 Min. :0.900 Min. :0.00000
## 1st Qu.:14.65 1st Qu.:32.50 1st Qu.:2.700 1st Qu.:0.00000
## Median :18.70 Median :41.00 Median :4.000 Median :0.00000
## Mean :18.18 Mean :44.82 Mean :4.029 Mean :0.01287
## 3rd Qu.:21.85 3rd Qu.:54.00 3rd Qu.:5.400 3rd Qu.:0.00000
## Max. :30.60 Max. :99.00 Max. :9.400 Max. :1.40000
## fire__no_yes
## 0:80
## 1:91
##
##
##
##
traindata <- ff[sample, ]
traindata <- traindata[, -c(1:4)]
summary(traindata)
## FFMC DMC DC ISI
## Min. :18.70 Min. : 1.10 Min. : 9.3 Min. : 0.00
## 1st Qu.:90.30 1st Qu.: 80.75 1st Qu.:474.9 1st Qu.: 6.30
## Median :91.70 Median :111.70 Median :661.8 Median : 8.40
## Mean :90.73 Mean :115.97 Mean :553.6 Mean : 9.15
## 3rd Qu.:93.10 3rd Qu.:146.97 3rd Qu.:713.9 3rd Qu.:11.30
## Max. :96.20 Max. :291.30 Max. :860.6 Max. :56.10
## temperature relative humidity wind speeds rain amount
## Min. : 2.20 Min. : 15.00 Min. :0.400 Min. :0.00000
## 1st Qu.:16.10 1st Qu.: 33.00 1st Qu.:2.700 1st Qu.:0.00000
## Median :19.60 Median : 42.00 Median :4.000 Median :0.00000
## Mean :19.24 Mean : 44.03 Mean :4.012 Mean :0.02601
## 3rd Qu.:23.30 3rd Qu.: 53.00 3rd Qu.:4.900 3rd Qu.:0.00000
## Max. :33.30 Max. :100.00 Max. :9.400 Max. :6.40000
## fire__no_yes
## 0:167
## 1:179
##
##
##
##
#View(traindata)
traindata2 <- traindata
colnames(traindata2) <- c("FFMC","DMC","DC", "ISI","Temp","Rel_Hum","Wind_Speed","Rain_Amt","Y/N")
traindata2$`Y/N` <- as.factor(traindata2$`Y/N`)
traindata2 <- as.data.frame(traindata2)
train_naibayes <- naiveBayes(traindata2$`Y/N` ~., data=traindata2, na.action = na.pass)
str(traindata2)
## 'data.frame': 346 obs. of 9 variables:
## $ FFMC : num 84 90.3 91.8 93.5 87.1 91.1 91.9 91.7 91.5 92.1 ...
## $ DMC : num 9.3 290 170.9 139.4 291.3 ...
## $ DC : num 34 855 692 594 861 ...
## $ ISI : num 2.1 7.4 13.7 20.3 4 5.8 8 7.8 10.7 9.6 ...
## $ Temp : num 13.9 19.9 20.6 17.6 17 23.4 21.4 17 17.1 17.4 ...
## $ Rel_Hum : num 40 44 59 52 67 22 38 27 43 57 ...
## $ Wind_Speed: num 5.4 3.1 0.9 5.8 4.9 2.7 2.7 4.9 5.4 4.5 ...
## $ Rain_Amt : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Y/N : Factor w/ 2 levels "0","1": 1 2 1 1 2 1 2 2 1 1 ...
#removing yes/no label to test
testdata2 <- testdata[,-9]
#Naive Bayes model Prediction
nb_Pred <- predict(train_naibayes,testdata2)
## Warning in predict.naiveBayes(train_naibayes, testdata2): Type mismatch
## between training and new data for variable 'Temp'. Did you use factors with
## numeric labels for training, and numeric values for new data?
## Warning in predict.naiveBayes(train_naibayes, testdata2): Type mismatch
## between training and new data for variable 'Rel_Hum'. Did you use factors
## with numeric labels for training, and numeric values for new data?
## Warning in predict.naiveBayes(train_naibayes, testdata2): Type mismatch
## between training and new data for variable 'Wind_Speed'. Did you use
## factors with numeric labels for training, and numeric values for new data?
## Warning in predict.naiveBayes(train_naibayes, testdata2): Type mismatch
## between training and new data for variable 'Rain_Amt'. Did you use factors
## with numeric labels for training, and numeric values for new data?
nb_Pred
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 1 1 1
## [36] 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1
## [71] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1
## [106] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1
## [141] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## Levels: 0 1
testsdata2 <- testdata[,-9]
#Testing accurancy of naive bayes model with Kaggle train data sub set
(confusionMatrix(nb_Pred, testdata$fire__no_yes))
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 18 22
## 1 62 69
##
## Accuracy : 0.5088
## 95% CI : (0.4313, 0.5859)
## No Information Rate : 0.5322
## P-Value [Acc > NIR] : 0.755
##
## Kappa : -0.0173
##
## Mcnemar's Test P-Value : 2.088e-05
##
## Sensitivity : 0.2250
## Specificity : 0.7582
## Pos Pred Value : 0.4500
## Neg Pred Value : 0.5267
## Prevalence : 0.4678
## Detection Rate : 0.1053
## Detection Prevalence : 0.2339
## Balanced Accuracy : 0.4916
##
## 'Positive' Class : 0
##
#Plot Variable performance
# X <- varImp(train_naibayes)
# X
# plot(X) <-sapply(y,as.factor)
y <- as.factor(y$fire__no_yes)
#model = train(x,y,'nb',trControl=trainControl(method='cv',number=10))
train_naibayes <- naiveBayes(traindata2$`Y/N` ~., data=traindata2, na.action = na.pass)
train_naibayes
##
## Naive Bayes Classifier for Discrete Predictors
##
## Call:
## naiveBayes.default(x = X, y = Y, laplace = laplace)
##
## A-priori probabilities:
## Y
## 0 1
## 0.482659 0.517341
##
## Conditional probabilities:
## FFMC
## Y [,1] [,2]
## 0 90.05269 7.482170
## 1 91.35251 3.311494
##
## DMC
## Y [,1] [,2]
## 0 109.5365 68.60180
## 1 121.9804 63.42465
##
## DC
## Y [,1] [,2]
## 0 523.6629 263.3780
## 1 581.5609 226.7034
##
## ISI
## Y [,1] [,2]
## 0 8.859281 5.523784
## 1 9.420670 4.140872
##
## Temp
## Y [,1] [,2]
## 0 18.62515 5.596134
## 1 19.81676 6.288258
##
## Rel_Hum
## Y [,1] [,2]
## 0 45.19760 17.54177
## 1 42.93296 14.96010
##
## Wind_Speed
## Y [,1] [,2]
## 0 3.958084 1.581875
## 1 4.062011 1.887305
##
## Rain_Amt
## Y [,1] [,2]
## 0 0.01556886 0.1052685
## 1 0.03575419 0.4783585
# str(model)
#Model Evaluation
#Predict testing set
Predict <- predict(train_naibayes,newdata = testdata )
## Warning in predict.naiveBayes(train_naibayes, newdata = testdata): Type
## mismatch between training and new data for variable 'Temp'. Did you use
## factors with numeric labels for training, and numeric values for new data?
## Warning in predict.naiveBayes(train_naibayes, newdata = testdata): Type
## mismatch between training and new data for variable 'Rel_Hum'. Did you use
## factors with numeric labels for training, and numeric values for new data?
## Warning in predict.naiveBayes(train_naibayes, newdata = testdata): Type
## mismatch between training and new data for variable 'Wind_Speed'. Did you
## use factors with numeric labels for training, and numeric values for new
## data?
## Warning in predict.naiveBayes(train_naibayes, newdata = testdata): Type
## mismatch between training and new data for variable 'Rain_Amt'. Did you use
## factors with numeric labels for training, and numeric values for new data?
#Get the confusion matrix to see accuracy value and other parameter values
Predict
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 1 1 1
## [36] 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1
## [71] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1
## [106] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1
## [141] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## Levels: 0 1
confusionMatrix(Predict, testdata$fire__no_yes )
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 18 22
## 1 62 69
##
## Accuracy : 0.5088
## 95% CI : (0.4313, 0.5859)
## No Information Rate : 0.5322
## P-Value [Acc > NIR] : 0.755
##
## Kappa : -0.0173
##
## Mcnemar's Test P-Value : 2.088e-05
##
## Sensitivity : 0.2250
## Specificity : 0.7582
## Pos Pred Value : 0.4500
## Neg Pred Value : 0.5267
## Prevalence : 0.4678
## Detection Rate : 0.1053
## Detection Prevalence : 0.2339
## Balanced Accuracy : 0.4916
##
## 'Positive' Class : 0
##
str(ff6)
## 'data.frame': 414 obs. of 9 variables:
## $ FFMC : num 88.8 91 92.8 88.2 84.6 90.3 75.1 91.7 92.1 91.2 ...
## $ DMC : num 147.3 129.5 73.2 55.2 26.4 ...
## $ DC : num 614 693 713 732 352 ...
## $ ISI : num 9 7 22.6 11.6 2 7.4 1.9 11.1 9.6 12.5 ...
## $ temperature : num 14.4 18.8 19.3 15.2 5.1 19.9 4.6 16.8 16.6 12.6 ...
## $ relative humidity: num 66 40 38 64 61 44 82 45 47 90 ...
## $ wind speeds : num 5.4 2.2 4 3.1 4.9 3.1 6.3 4.5 0.9 7.6 ...
## $ rain amount : num 0 0 0 0 0 0 0 0 0 0.2 ...
## $ fire__no_yes : num 0 1 0 1 1 1 1 1 1 0 ...
ff6$fire__no_yes <- as.factor(ff6$fire__no_yes)
IG.CORElearn <- attrEval(ff6$fire__no_yes ~ ., data=ff6, estimator = "InfGain")
IG.RWeka <- InfoGainAttributeEval(Species ~ ., data=iris,)
IG.FSelector <- information.gain(Species ~ ., data=iris,)
IG.CORElearn
## FFMC DMC DC ISI
## 0.009286285 0.012383127 0.012410011 0.010643077
## temperature relative humidity wind speeds rain amount
## 0.016756744 0.013328263 0.015651330 0.003070871